Human Interaction & Gait Strategy with Tightly-Coupled

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Human interaction & gait strategy with tightly-coupled lower-extremity systems by Aditi Gupta B.S., University of California, San Diego (2015) Submitted to the Harvard-MIT Program in Health Sciences & Technology in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the

Author...... Harvard-MIT Program in Health Sciences & Technology October 30, 2020

Certified by...... Leia A. Stirling, PhD Associate Professor, Industrial & Operations Engineering University of Michigan Thesis Supervisor

Accepted by...... Emery N. Brown, MD, PhD Director, Harvard-MIT Program in Health Sciences & Technology Professor of Computational Neuroscience and Health Sciences & Technology 2 Human interaction & gait strategy with tightly-coupled lower-extremity systems Aditi Gupta

Interest in the use of exoskeletons (wearable robotic devices tightly-coupled to the user’s body) for human gait augmentation has soared recently, with research flourishing in system design, control, and use efficacy. Use cases span many fields, from military (e.g. load carriage assistance) to medicine (e.g. gait rehabilitation or restoration) and industry (e.g. injury prevention). Evaluating the human factors of human-exoskeleton interaction is an essential step towards operationalization. Unexplained variation in gait strategy and adaptation observed across individual operators must be better understood to enable safe and effective exoskeleton use in real-life environments. Cognitive fit is an individuals’ understanding and ability to operate a system. Exoskele- tons and similar tightly-coupled lower-extremity (TCLE) systems entail new interaction modalities that may affect cognitive fit. This thesis explores how cognitive factors andal- ternative interaction modalities impact individuals’ gait and task performance. Two studies were conducted, one evaluating inhibitory control as measured by a modified Simon task using interaction modalities relevant to TCLE system use, i.e. tactile cues and lower-extremity responses. Second, the Human-Exoskeleton Strategy & Adaptation (HESA) study was implemented, in which individuals completed tasks assessing cognitive factors, i.e. inhibitory control and attention, then walked with an ankle exoskeleton. Evaluation of inhibitory control with tactile cues and lower-extremity responses resulted in slower response times and decreased response accuracy. A probe of attention in the HESA study, i.e. completion of a walking task on a self-paced treadmill, showed modified gait characteristics under increased attentional loads, particularly at slower walking speeds and with the addition of a secondary task. Individualized variation in exoskeleton gait, quantified by spatiotemporal gait characteristics, was explicitly presented for the first time, showing that distinct individuals initially prioritize goals like stability and coordination with an ankle exoskeleton differently. Finally, select measures of cognitive function were found to be correlated to individuals’ exoskeleton gait strategy. Individual differences in baseline factors like inhibitory control and ability to perform tasks under divided attention impact individuals’ cognitive fit with exoskeleton systems. This individualized variation, as well as broader population patterns, should inform exoskeleton design and training by encouraging gait strategies that support desired exoskeleton use goals. For example, stroke patients using an exoskeleton to restore their gait and mitigate fall risk should prioritize stability during system use, while factory workers should prioritize system coordination to minimize injury risk. This thesis provides foundational insights into human-exoskeleton interaction and gait strategy from a human factors perspective.

Thesis Supervisor: Leia A. Stirling

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. This material is based upon work supported by the Department of the Army under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Army. 3 4 Acknowledgments

This PhD has been quite the roller coaster, and roller coasters are much more fun with a cart full of family, friends, and colleagues. I have been lucky in that my co-riders have been wonderful, supportive, and kinder than I could have ever hoped. All of this first started when I walked into Prof. Leia Stirling’s office one day, asagreen first year graduate student. I nervously declared that I knew nothing about biomechanics, but I was really interested in her work on human motion, if she would have me. Leia has always chastised me, and all her students, whenever we put ourselves down by saying we ‘don’t know anything’. It wasn’t until recently that I realized how right she was. Life, work, and education aren’t about what you know. What matters much more is how much you are willing to learn. You have something to offer, even in a completely unfamiliar space, aslong as you’re willing to do the work. Leia taught me this, and so much more in these last five years. I will be forever be grateful for her deep empathy as a research supervisor, always willing to listen when things aren’t going well personally or professionally; for her unyielding pressure to push me to be better, always; for her unwavering mentorship and support, even in the middle of her own personal and professional transition; and for her humanity. In an academic world where work and competition can define individuals, she created a space for us to flourish, not only as researchers, but as human beings, and I will forever be grateful. I must also thank Dr. Ryan McKindles, who, from nearly the very beginning, served as a co-advisor to me. He was an invaluable resource without whom this work would not be possible. Always willing to lend an ear, give advice on science communication, connect me with his network as I explored career options, or just be there to listen when things got tough, Ryan has been an integral part of my growth these past five years. I feel so lucky to have had not one, but two, incredible mentors during my graduate career. Thanks must also go to my committee members, Profs. Julie Shah and Danielle Wood. Julie has always struck me with her unending kindness and her ability to make me feel optimistic about my research every single time we spoke. Danielle has been an inspiration. She showed me, by example, that I can be a scientist while simultaneously working towards and having an impact on issues of social justice and equity that I care so deeply for, a goal I struggled to even articulate before I met Danielle. It comforts me to know that there are mentors like Leia, Ryan, Julie, and Danielle training tomorrow’s scientists, instilling in them the same kindness, caring, and sense of purpose my committee has shown to me. In the same vein, I must thank mentors I’ve had over the course of my education, without whom I would not be here today. Drs. Ebonee Williams, Pedro Cabrales, and Douglas Nitz from UC San Diego helped to me access and put to good use a plethora of research, leadership, and life experiences. It is thanks to them I made it to MIT in the first place. I found myself in the unique position of working with an amazing team of technical experts at CCDC Soldier Center and MIT Lincoln Lab who helped me build and implement an ambitious human-exoskeleton study. This work would not exist without Harvey Edwards and Aaron Rodriguez, and I am forever indebted to them for all the technical expertise they provided in making this thesis a reality. No academic progress is possible without the unsung, but always dedicated staff that help hold the entire enterprise together. Beata Shuster, Liz Zotos, and the HST Academic Office provided invaluable logistical support without which I would have spent all mytime figuring out how to submit reimbursements (they’re a lot of work!). I had the pleasure of working with and mentoring six different undergraduate students

5 during my tenure as a graduate student at MIT. Sarah Gonzalez, Alvin Harvey, and Thomas Rick were phenomenal undergraduate engineers that built impressive devices to enable data collection and implementation of the experiments presented in this thesis. Sarah Lincoln was a joy to mentor as an excited and inquisitive first year, who taught me so much about what it means to be a good mentor. Matthew Leurman and Claire McGinnity built cleaner code bases for data analyses than I ever could, and pushed me to think more creatively and deeply about the data we were examining. A special shout out goes to Alvin, who Leia and I mentored through the MIT Summer Research Program and who is now an MIT graduate student himself. I prioritized two things above all in searching for a lab during my first year in the HST program: a caring, supportive, and active advisor (check!), and a friendly, collaborative, and balanced lab environment. The Human Systems Lab checked all these boxes and more, providing a community that supported me through the hard times, and celebrated all the good times. Special thanks to Paul Stegall and Hosea Siu, for their scientific mentorship and willingness to answer all my technical questions; to Chika Eke, my forever friend who, despite leaving me after only a year to go get a ‘real’ job, first made me feel at home in the HSL; and to Tim McGrath and Richard Fineman, the best office mates anyone could ask for, who provided laughs, commiseration, profound (and sometimes facetious) conversation, and everything in between. As I reflect upon my experiences at MIT, the research is but one aspect of anextremely fulfilling 5+ years in Boston. I sought out and found a number of outstanding communities at MIT and across the greater Boston area, without whom I would not have made it through the last few years. The Boston dance community provided me an outlet to channel all my stresses into movement. It allowed me to grow my appreciation for dance and reminded me of my love for performance, music, and art. MIT Global Startup Workshop taught me more professional and life skills than anything else I have ever experienced, save for perhaps the PhD itself. MIT GSW also helped me to achieve a personal goal I set for myself upon starting graduate school - to travel to at least one new country each year. It gave me the chance to not only meet, but work closely with people across the globe. Finally, MIT GSW gifted me with incomparable friends and mentors. Rami, Kiran, MJ, Shaurya, David, Diana, Juan, Megan, Georgie, and Lee, thank you for all that you taught me about leadership, relationship management, cross-cultural collaboration, keeping my cool under high stakes and intense stress, and so much more. And of course, thank you for your friendship. I cannot forget the MIT-wide DEI community, a group of tenacious, passionate, and tireless individuals who relentlessly push, often against adamant barriers, to make MIT a more inclusive, equitable, and just place. Libby, Bianca, my fellow members of GradSAGE, and HST Div - Erin, Christian, Lucy, and Claudia - deserve more thanks than I can give. Together, we have pushed the needle on diversity, equity, and inclusion at MIT, all while sharing and providing crucial emotional support in trying times and circumstances. Though there is much work left to be done, I take solace in the fact that there are others who care about this work and recognize its importance. I can only hope that, through their mammoth efforts, one day MIT will be inclusive and equitable for people of all backgrounds andwalks of life. These are my people. And without my people, I would not be who I am. Finishing this thesis in 2020 has been an... experience. I am certain each and every one of us will remember, forever, the relentlessness of the news and world events that have shaped this year, from a global pandemic to political turmoil and injustices across the country and the world. As with any good roller coaster, the biggest climbs, loops, and drops come at the very end, and so has it been with my PhD. While there have been extreme highs, I have

6 also experienced some of my lowest lows in the past year, and were it not for friends, I don’t know where I would be. Lucy, Claudia, Ellen, MJ, Chika, Shelley, Western, Saria, Bianca, Divya, Chetna, and everyone else who has lent an ear, a hand, or a warm hug this last year: words cannot express what you mean to me. You’ve shaped me, you’ve built me up, and you’ve sustained me throughout these last few months (if not years and decades). I would not be where or who I am without you, and I look forward to seeing where life takes us together. Last but never least, I must thank my wonderful, supportive family - both nuclear and extended - that I was so lucky to fall into. The Bajaj’s, Singh’s, and Gupta’s taught me what unconditional love truly means and growing up with you all has been the privilege of a lifetime. Home will forever be wherever you are. I don’t know what I did in a previous life to deserve you all, but it must’ve been pretty damn great. The core of that extended family are my parents and my sister. We fight, we laugh, we throw things at each other, but without your support and love, I would be nothing. Our family immigrated to the US when I was just three years old, and my parents left their own families on the other side of the world so that my sister and I would have greater opportunity. My dad was my first role model. He worked the night shift while getting his own PhDso he could support us. He made me believe from the very beginning that I was powerful and that I could do whatever I set my mind to, no matter what the world may say. My mom is one of the strongest and most selfless people I know. She made unimaginable sacrifices for our family, to take care of us and let us soar, sometimes at the expense of herself. I will never be able to repay that debt, though I will never stop trying to live up to it. My little sister inherited that strength, and she showed me that it’s not always your elders that you learn from - the young ones have something just as important to say. All that I am today, and all that I have accomplished, is because of my mentors, my family, and my friends. You have supported me, you have inspired me, you have shown me the way. Thank you.

7 8 Contents

1 Introduction 13 1.1 Definition and Uses of TCLE Systems ...... 14 1.2 TCLE Systems as a Subset of Human-Robot Interaction ...... 16 1.3 Cognitive Fit ...... 18 1.3.1 Inhibitory Control and Attention ...... 19 1.3.2 Cognitive Fit with Alternative Interaction Modes ...... 22 1.4 Human Gait Strategy and Motor Adaptation ...... 22 1.4.1 Gait Under Divided Attention ...... 25 1.4.2 Exoskeleton-Augmented Gait ...... 26 1.5 Thesis Objectives and Aims ...... 28

2 Human Cognition & Performance in Alternative Interaction Modes 31 2.1 Executive Function and the Simon Effect ...... 32 2.1.1 The Simon Task ...... 32 2.2 Stimulus-Response Compatibility and System Design ...... 33 2.2.1 Effect of Alternative Interaction Modalities ...... 33 2.3 Materials and Methods ...... 34 2.3.1 Participants ...... 34 2.3.2 Experimental Protocol ...... 35 2.3.3 Data Analysis ...... 36 2.4 Results ...... 38 2.4.1 Response Times ...... 38 2.4.2 Accuracy and Response Rate ...... 40 2.4.3 Cognitive Processing Time ...... 41 2.5 Discussion ...... 42 2.6 Conclusion ...... 45

3 The Human-Exoskeleton Strategy & Adaptation Study 47 3.1 Protocol Overview ...... 48

9 3.2 Participants ...... 48 3.3 Materials ...... 50 3.3.1 Data Collection Systems ...... 50 3.3.2 The Dephy Ankle Exoskeleton ...... 50 3.4 Methods ...... 51 3.4.1 Modified Simon Task ...... 52 3.4.2 Self-Pacing and Self-Paced Dual Tasking ...... 52 3.4.3 Initial Exoskeleton Walking ...... 53 3.5 HESA Study in the Context of this Thesis ...... 54

4 Gait Strategy & Dual Tasking with a Self-paced Treadmill 57 4.1 Gait Modifications on Self-Paced Treadmills ...... 58 4.2 Gait and Attention ...... 58 4.3 Dual Tasking During Ambulation ...... 59 4.4 Materials and Methods ...... 61 4.4.1 Experimental Protocol ...... 61 4.4.2 Data Analysis ...... 63 4.5 Results ...... 65 4.5.1 Learning Effects ...... 65 4.5.2 Effect of Speed on Dual Task Performance ...... 66 4.5.3 Effect of Speed and Dual Task on Speed Achievement and Gait Strategy 67 4.6 Discussion ...... 72 4.7 Conclusion ...... 77

5 Individualized Exoskeleton Gait Strategies 79 5.1 Probing Gait Strategy via Observable Gait Characteristics ...... 80 5.1.1 Individual Variation in Exoskeleton Gait ...... 80 5.1.2 Stability ...... 81 5.2 Materials and Methods ...... 82 5.2.1 Experimental Protocol ...... 82 5.2.2 Data Analysis ...... 83 5.3 Results ...... 84 5.3.1 Effect of Exoskeleton Power State ...... 84 5.3.2 Effect of Walking Speed ...... 86 5.4 Discussion ...... 89 5.5 Conclusion ...... 94

10 6 Relationships between Cognitive Factors & Exoskeleton Gait 95 6.1 Cognitive Factors and Physical Activity ...... 96 6.2 Balancing Gait Goals During Exoskeleton Use ...... 97 6.3 Methods ...... 99 6.3.1 Baseline Parameters ...... 99 6.3.2 Intra- versus Inter-subject Variability ...... 100 6.3.3 Relationships Between Baseline and Exoskeleton Gait Parameters . . . 103 6.4 Results ...... 105 6.4.1 Intra- vs. Inter-subject Variability ...... 105 6.4.2 Baseline and Exoskeleton Gait Parameter Correlations ...... 105 6.5 Discussion ...... 108 6.5.1 Limitations ...... 116 6.6 Conclusion ...... 117

7 Conclusions & Future Work 119 7.1 Summary of Results ...... 120 7.1.1 Human Cognition & Performance in Alternative Interaction Modes . . 120 7.1.2 The Human-Exoskeleton Strategy & Adaptation Study ...... 121 7.1.3 Gait Strategy & Dual Tasking with a Self-paced Treadmill ...... 121 7.1.4 Individualized Exoskeleton Gait Strategies ...... 121 7.1.5 Relationships between Cognitive Factors & Exoskeleton Gait . . . . . 122 7.2 Contributions ...... 123 7.3 Applications and Recommendations ...... 124 7.4 Potential Future Work ...... 126 7.4.1 Additional Exoskeleton Gait Characteristics ...... 126 7.4.2 Perceptual Factors Underlying TCLE System Use ...... 127 7.4.3 Gait Adaptation with TCLE Systems ...... 127 7.4.4 Longitudinal Use of TCLE Systems ...... 128 7.4.5 Creation of a Cognitive Fit Checklist ...... 129 7.4.6 Extension to Patient Populations ...... 129 7.5 Concluding Remarks ...... 130

Bibliography 132

A Supplementary Figures and Tables 149 A.1 Generalized Linear Mixed Effects Model for SP & SPDT Data ...... 149 A.2 Initial Exoskeleton Walking Box Plots ...... 152 A.3 Baseline and Exoskeleton Parameter Correlation Plots ...... 160

11 B HESA Study Task Protocols 167 B.1 Perceptual Tests ...... 167 B.1.1 Semmes-Weinstein Monofilament Test ...... 168 B.1.2 Ankle Angle Replication Test ...... 168 B.2 Motor Tasks ...... 171 B.2.1 Static Balance ...... 172 B.2.2 Split-Belt Adaptation ...... 173 B.3 Varied Speed Exoskeleton Walking ...... 173

C Study Questionnaires 175 C.1 Pre-experiment Questionnaire ...... 175 C.2 Cognitive Surveys ...... 178 C.2.1 Reinvestment Propensity Survey ...... 178 C.2.2 Technology Adoption Survey ...... 179 C.2.3 Dephy Exoskeleton Expectations Survey ...... 179 C.2.4 Perceived Fluency Survey ...... 180

List of Figures 182

List of Tables 191

12 Chapter 1

Introduction

This project began with one broad, overarching question: why do some people easily and quickly adapt to walking with exoskeletons, while others do not? We hypothesized that a variety of sensorimotor and cognitive factors underlie the wide individual variation observed by exoskeleton researchers. Sensory thresholds, proprioceptive ability, cognitive functions, baseline motor adaptability, and more were all discussed as good candidates and worthy of exploration. And thus, we conceived of an ambitious, two-day study in which participants would complete a battery of sensorimotor and cognitive tests and, at the very end, walk with a lower-extremity exoskeleton. A subset of the data collected in that study are presented in this thesis, specifically with the goal of exploring the following set of foundational questions relating to human interaction with exoskeletons and other tightly-coupled, lower extremity (TCLE) systems:

1. How do alternative interaction modalities impact peoples’ ability to complete goal- oriented tasks with human-machine systems?

2. How do tightly-coupled lower-extremity systems impact individuals’ gait strategy and ability to perform goal-oriented tasks?

3. What cognitive factors underlie individualized variation in gait strategy with lower- extremity exoskeletons?

Interest in the use of exoskeletons in operational environments is of great interest, but a number of factors must be considered before such systems can become ubiquitous. First,

13 much unexplained individual variability is observed during exoskeleton operation [90], making it difficult to know who may be a good candidate to operate an exoskeleton inany given context. For example, there is interest in medicine to utilize exoskeletons for rehabilitation or gait restoration purposes. Clinical indications, i.e. the facts and interpretations of a patient’s condition that provide a reasonable basis for intervention, are of paramount importance in determining medical intervention [137]. The idea is that individual factors (e.g. health status or likelihood of sticking to a therapeutic regimen) are factored into the decision to prescribe certain therapies. In the same vein, it is important to understand how individual factors (e.g. health status, level of physical functionality, or cognitive capabilities) may be related to a patient’s ability to safety and effectively use an exoskeleton. Such knowledge can provide frameworks for decision-making around the when prescription of exoskeleton use may be warranted or effective in medical scenarios. While this thesis focuses on a healthy subject population, the outcomes can and should be used as a foundation to further knowledge on exoskeleton interventions for patients that may benefit from such systems.

An understanding of how individuals’ interact with exoskeletons and other TCLE systems that entail alternative system interaction modalities, i.e. tactile feedback and lower- extremity responses, also provides foundational knowledge that can inform system design and training methodologies. Currently there are no broadly accepted exoskeleton design or testing standards [143], nor are there standard training methods for exoskeleton use in operational environments, which are distinct from the use of exoskeletons for limb training purposes, such as during gait training and rehabilitation for stroke or spinal cord injury patients [8, 71]. This thesis aims to provide some of that foundational knowledge on human interaction with exoskeletons and other TCLE systems.

1.1 Definition and Uses of TCLE Systems

Here we define tightly-coupled lower-extremity systems as any mechanical device that is physically coupled to an operator’s body in such a way that movement of either the device or the person invokes motion of the other, specifically the person’s legs in the case of lower-extremity systems (Figure 1-1). TCLE systems include devices like exoskeletons and treadmills, both of which impact an individuals’ gait and whose state in turn can be

14 Figure 1-1: When utilizing a TCLE system, movement of either the human or the system necessarily invokes movement of the other. For example, in the context of wearable robotic device like an exoskeleton, if the operator takes a step, the exoskeleton must move as it is positioned on the operator’s leg, resulting in an interaction at the back of the person’s leg as the leg pushes the exoskeleton back. If the exoskeleton actuates first and moves backward, the operator’s leg must move in tandem with that actuation because the leg is encased within the system. This scenario results in a physical interaction at the front of the person’s leg because, in this case, the exoskeleton is pushing the user’s leg back.

modulated by an individuals’ gait (in the case of treadmills, this applies to systems that are self-paced, or use the person’s position on the treadmill as the speed controller).

TCLE systems have great potential as augmentative devices that can improve human gait and task performance in real-life scenarios. Lower-extremity exoskeletons have applications in clinical rehabilitation for stroke and spinal cord injury patients [7, 28, 53], assistance in load carriage and metabolic cost reductions (e.g. to enhance soldier performance) [62, 85, 110, 122], and preventing injury in industrial settings [37]. Treadmills, both fixed-pace and self-paced, are also useful TCLE systems. They are commonly used as research vehicles to better understand aspects of gait and environmental factors (e.g. visual feedback or external distractions) that can impact gait [115, 112]. Treadmills are also extremely important tools for training and rehabilitation to help patients relearn safe and healthy ambulation in cases of stroke, spinal cord injury, or other illnesses that impact individuals’ ability to walk [108, 155].

To maximize TCLE system effectiveness in operational and training scenarios, it ises- sential to understand how system operation may impact gait strategy and goal-oriented task performance in an operational context.

15 1.2 TCLE Systems as a Subset of Human-Robot Interaction

Human-robot interaction (HRI) explores the physical, cognitive and social interactions between humans and robotic systems with the goal of understanding and improving aspects of those interactions and overall human-robot task performance. Evaluation of interactions between humans and robots must carefully consider the nature of the interaction. For example, robotic systems can be co-located with the human, or the interaction can be remote [64]. Methods and metrics for assessing HRI in these two contexts must be consider very different factors. Remote robot operation likely does not require consideration of thephys- ical safety of the human, at least with respect to the impact of the system’s physicality. Assessment of HRI for co-located systems must consider various factors that may impact the interaction, such as the type of task(s) the human and robot are completing and under what physical, cognitive, and/or social constraints. Human interaction with TCLE systems can be considered a subset of HRI with unique considerations and characteristics. Here we focus specifically on physically tightly-coupled robotics systems, meaning these systems are tightly coupled to the body of the operator (e.g. wearable systems like exoskeletons). Interactions with physically tightly-coupled systems have the unique and important feature that movement of either the human or of the robotic system necessarily invokes movement of the other. This tight physical coupling is an essential consideration in HRI assessment within the context of TCLE systems because that coupling can uniquely impact individuals’ physical and cognitive interactions. For example, tightly-coupled systems that sit on the lower-extremities can have effects on stability or perceived safety during walking, an extremely common human function.

HRI provides a foundation to begin assessing and evaluating TCLE systems. There already exist a number of objective and subjective metrics that assess how well a robot and a human are working together, as well as the quality and effectiveness of the interaction. However, these metrics are often system- or task-dependent and require adjustment for TCLE systems. For example, relational factors such as safety, trust, and fluency between the human and the robotic system enable evaluation of the quality of interaction between the two entities. Each of these factors can be measured using quantitative and qualitative (e.g. survey- or observation-based) metrics. However, interpretation of these metrics cannot be universally applied, rather implementation and evaluation must be adapted for both the

16 system in question and the task goal of the human-system interaction.

Safety and fluency are two example constructs commonly used to evaluate system performance in HRI that are extremely relevant to TCLE systems, but may require significant restructuring for direct applicability. Physical safety in HRI focuses on preventing unwanted or unintentional contact between the human and the system, e.g., collision prevention [163]. This immediately assumes that the human and the robot are physically distinct, an assumption not met in TCLE systems. In the context of a TCLE system, physical safety takes on an entirely new meaning and requires very different evaluation metrics. The goal of preventing collisions with an exoskeleton, for example, could be redefined to prevent interactions that convey excessively large or dangerous amounts of force. Thus, common HRI safety measures such as intention-based controls to minimize physical contact between the human and the robot in shared work spaces [5] or design considerations for those work spaces themselves [119] cannot be applied to TCLE systems, whereas control of robot movement factors such as torques/forces and positional control [12] are more directly applicable. However, the concept of safety in human-TCLE system interaction is essential, especially because TCLE systems can impact gait stability and balance, leading to fall risk.

Fluency metrics may also require significant alteration in the context of TCLE systems. Fluency is defined in HRI as a high level of coordination between the human and therobot, which results in a well-synchronized meshing of actions [78]. Fluency can be quantified with a variety of objective (e.g. concurrent action, human/robot idle time) and subjective (e.g. the humans’ perceived levels of fluency and comfort with the robot) metrics. Increased fluency as measured by these and other metrics can increase task performance. For example, by implementing robotic nonverbal cues that improved fluency, task performance also increased [21]. Adaptive controllers which learn from the human have also been shown to increase individuals’ perception of fluency and system trust, as well as objective fluency measures [79, 131]. However, interpretation of objective measures like idle time and concurrent action are extremely task dependent. For example, in a fetch-and-deliver task, lower idle times and high concurrent action may be desired and therefore defined as high fluency [192]. Idle time and concurrent action require different interpretations for TCLE systems, in which relevant idle and concurrent action timescales are much smaller than in physically-distinct robots. Siu et al. [175, 176] have adapted fluency in the context of human-exoskeleton use by focusing on differences in timing of actuation and movement, further indicating thatHRI,

17 with added considerations for TCLE systems, provides a foundation from which to begin assessing human gait and interaction with TCLE systems. HRI measures like safety and fluency can be related to cognitive factors like attention. Low levels of trust or fluency with a TCLE system may lead the user to attend moretothe actions of the TCLE system during operation. Greater attention placed on the operation of the TCLE system limits attention available for gait modulation or the completion of secondary goal-oriented tasks. This limitation on attention can in turn greatly impact individuals’ gait and task performance with TCLE systems. In the next sections we provide further background on cognitive factors relevant to TCLE systems and the role of attention in gait.

1.3 Cognitive Fit

For human-system operation to be effective and successful, there must be a high level of‘fit’ between the system and the user. The idea of good ‘fit’ is broadly defined and dependent upon a host of factors including but not limited to specifics of the system in question (i.e. clothing fit versus fit of workplace surroundings for optimal ergonomics) and the presence or lack of motion. Stirling et al. [186] defines three characteristics of fit in the context of exoskeletons: static fit, dynamic fit, and cognitive fit. Static and dynamic fit focus on the physical interactions between a system andthe operator. Static fit refers to the alignment of body segments with corresponding wearable system components in the absence of motion, such as body fit of spacesuits [14, 91]. In the context of an exoskeleton, this may look like a requirement that the knee hinge should be properly aligned with the human’s knee joint. Dynamic fit considers how changes in positioning within the context of functional activities impacts the full range of the positional relationship between the human and the TCLE system. For example, the knee and ankle hinges of a given exoskeleton may be perfectly in-line with the wearer’s knee and ankle joints during quiet standing, but those hinges and joints will move through a variety of relational positions during gait. Different levels of dynamic fit can impact task performance, asseen in spacesuit walking and functional tactile tasks [56, 102]. Therefore, there must be a broad range of motion and positional relationships within which the human operator can safely and effectively walk and complete goal-oriented tasks with a TCLE system.

18 Figure 1-2: Theoretical schematic of perception-cognition-action pathways relevant to TCLE system use and necessary for good cognitive fit. System users must be able to sense thecues of the system on their lower extremities (perception), comprehend those cues and integrate those with environmental information to make action decisions (cognition), and carry out those decisions (action).

While aspects of static and dynamic fit can inform cognitive fit, for example, physical interaction pressures can impact user’s comfort and mental workload with exoskeletons [168], cognitive fit takes one further step and requires systems to support the perception-cognition- action pathways of the user [186]. Cognitive fit therefore must inclusively consider sensory elements such as somatosensation and proprioception, cognitive elements such as executive function and attention; and motor elements including motor action selection (Figure 1-2). Here we focus and expand upon cognitive elements.

1.3.1 Inhibitory Control and Attention

Two cognitive factors, inhibitory control and attention, can enable good cognitive fit and are essential for TCLE system operation. Inhibitory control is the ability to ignore irrelevant stimuli and prevent incorrect responses [129] and is essential when operating complex systems. For example, inhibition allows individuals to drive a car without becoming over-

19 whelmed by the many multi-modal stimuli being presented simultaneously [55]. Inhibitory control can be subdivided into response inhibition and attentional inhibition [41], both of which are essential to TCLE system operation. The first, response inhibition, enables individuals to prevent automatic or incorrect motor responses. TCLE systems provide unfamiliar interactions that may result in inappropriate reflexive responses, for example, a reflexive response to jerk away from an unexpected touch or pressure from a wearable device, that must be inhibited for proper system operation. Inhibitory control is also essential in ignoring stimuli irrelevant to the task at hand, i.e. attentional inhibition. For example, someone walking with an exoskeleton will perceive pressure and interaction forces from the wearable system during ambulation. It is essential that the user (i) understand when those forces are important to attend to, such as if those forces indicate that the system is malfunctioning and requires intervention from the user, and (ii) be able to ignore those interactions when they are irrelevant and do not materially impact the user’s task goal.

Evaluation of inhibitory control can provide important information regarding how well an individual may utilize an exoskeleton. For example, individuals who are more impulsive generally take longer to inhibit inappropriate responses [101]. Those individuals who show less inhibitory control may be less cautious with TCLE systems, or perhaps more open to allowing the system to change their behavior. Evaluating inhibitory control can be difficult, as it is a cognitive construct and cannot be directly measured. However, different facets of inhibitory control can be observed via test outcomes that probe individuals’ ability to ignore irrelevant stimuli or inhibit responses. For example, stop-signal tasks can probe response inhibition by measuring the latency required in the presentation of a stop signal to enable inhibition of a response [195]. Another method, the Simon task, probes aspects of both attentional and response inhibition. The Simon task presents right- or left-sided cues with spatially conflicting information, for example by presenting arrows on different sidesofa screen (Figure 1-3) or auditory cues in different locations. Differences in reaction timeto congruent versus incongruent cues provide a view of an individuals’ ability to ignore that irrelevant spatial cue, as well as inhibit an incorrect response to that cue [172, 171]. Use of such tests can provide insight into individual’s level of inhibitory control, a cognitive function that may underlie individuals’ use of exoskeletons.

Attention is an individuals’ capacity to process information and is especially necessary when using TCLE systems in operational environments. The ability to direct attention can

20 Figure 1-3: Example of the four different cues presented during a visual Simon task. The required response is the direction the arrow points. Congruent cues present the arrow spatially on the side of the directed response, while incongruent cues present the arrow in the spatially incongruous position. greatly impact task performance in complex real-life environments with external distractors that require simultaneous completion of a multitude of tasks. Multiple resource theory (MRT) states that individuals have a finite amount of information processing capacity, i.e. attention, and the addition of numerous tasks and cues necessarily leads to losses in task performance [198] due to division of attention amongst those tasks. Modalities of task input (e.g. visual or auditory cues) and output (e.g. verbal or manual response) are integral to MRT, particularly in that tasks in the same modality (e.g. two visual tasks with a manual response) interfere more so and result in worse task performance than two tasks in different modalities (e.g. one task with visual cue/manual response and a secondary task with auditory cue/verbal response) [200, 199].

Dual task testing paradigms provide evidence of divided attention in the presence of multiple tasks [39], and outcomes from dual task paradigms can be utilized as predictors of task performance, e.g. in the context of flight performance for pilots [133]. With regards to gait, though there is some level of automaticity with rhythmic activities like gait [43], studies on older adults indicate that higher level cognitive functions are necessary to the control of gait [203]. This suggests that ambulation requires a non-negligible level of attention. Addition of a TCLE system therefore may change the level of attention required. A subset of dual task paradigms incorporate gait as a primary task with the goal of assessing attention required for gait, as well as attention required for various types of secondary tasks during ambulation. Given the importance of modality in MRT, the modality of secondary task cue presentation or response may impact either individuals’ gait or their secondary task

21 performance. Thus, selection of a relevant and appropriate secondary task modality is essential to assessment of TCLE systems. Further discussion of gait and dual task paradigms can be found in Section 1.4.1.

1.3.2 Cognitive Fit with Alternative Interaction Modes

Cognitive fit is highly dependent upon individuals’ ability to perceive and comprehend cues from a system. When those cues are presented in a way that is unfamiliar to the user, cue comprehension and motor selection may become more difficult. TCLE systems provide tactile cues, which is a cue presentation mode not commonly utilized in daily life whereas visual and auditory cues are more common (e.g. stop-lights, cross-walk signs, sirens, etc.). How- ever, tactile cues play an essential role in many human-machine systems. Tactile cues can increase immersion (i.e. the feeling of being present [118]), in virtual reality environments [187]. Tactile cues are also used to convey important system information to the user, such as during the operation of surgical simulators [188] and aviation displays [177]. The addition of tactile cues can even improve task performance, e.g. enabling more accurate tele-operated needle insertion [136]. Thus, it is essential to understand how alternative interaction modalities impact user’s perception and understanding of system form and function to enable good cognitive fit and thus encourage high levels of task performance with TCLE systems.

1.4 Human Gait Strategy and Motor Adaptation

The terms strategy and adaptation are ill defined in gait literature and different investigators utilize these terms in distinct ways. Motor adaptation can be defined as a trial-to-trial adjustment of movement based on error feedback [9]. These fine adjustments may lead toa more global change in the way someone walks, or a convergence to one method of walking. Others point to the importance of variability, noise, and exploration of strategy in gait movements as a sign of motor adaptation [184]. Both of these definitions can broadly be applied to many types of movements, but there remains a lack of clarity in usage of the terms ‘strategy’ and ‘adaptation’. In some cases, these terms are used interchangeably. However, to provide some specificity in how these terms are utilized in this thesis, we proposea specific relationship between the two. We will use the term gait strategy to mean a weighted combination of observable features that together describe a specific approach to completing

22 Figure 1-4: Schematic describing the relationship between gait strategy and adaptation during theoretical walking with an exoskeleton. The blue line shows changes in a gait characteristic over time. A weighted combination of this gait characteristic and others comprises a gait strategy. The change in this characteristic, and gait strategy overall, over time is termed adaptation. This change can be conscious or subconscious. For example, at A when the exoskeleton is turned on, the gait characteristic may change subconsciously, whereas the individual makes a conscious decision to modulate their gait near B. a task. For example, the combination of muscle activation patterns, stride lengths, and joint angle profiles that make up an individuals’ gait during operation of an exoskeleton comprise that individuals’ gait strategy with the exoskeleton. We term the features themselves (i.e. muscle activation patterns, stride lengths, and joint angle profiles) gait characteristics, which can be observed and quantified. Gait adaptation is defined here are a change in these characteristics (and overall gait strategy) over time (Figure 1-4). Different gait strategies can be used to accomplish varying gait goals, or specific desired gait tasks or priorities. A particular gait goal can be accomplished using any number of gait strategies. For example, walking with a lower-extremity exoskeleton without falling can be accomplished using some combination of anteroposterior (front-back) and mediolateral (side-to-side) stability. Further uses of the terms gait characteristics, strategy, adaptation, and goal in this thesis will follow these definitions.

Now, while there are not broadly accepted definitions of gait strategy and adaptation, it is unequivocal that aspects of gait strategy and adaptation, as measured by observable gait characteristics, change in response to many different types of factors, both endogenous to the human and externally provided. Endogenous factors such as age, ability and pathology all result in varying changes to individuals’ ability to ambulate and their gait patterns.

23 Older adults often walk at slower baseline speeds than young adults [77, 18]. Their gait patterns change as well - older adults or individuals with injuries or pathologies that limit ambulation may select gait characteristics (i.e. stride lengths or stride widths) that enable greater stability [89] to minimize fall risk or metabolic cost [142]. External factors such as environmental perturbations can also have significant impacts on individuals’ gait patterns. While we focus here on external factors and their impact on gait, it is important to consider how results from this work, which focus on exoskeleton-augmented gait in healthy young adults, can and should be used to inform the design and implementation of exoskeletons to assist users who may be disabled, elderly, or have any variety of gait restrictions that could benefit from external augmentation.

Gait strategy and adaptation can be greatly impacted by external factors like physical perturbations. Investigations with treadmills can provide such perturbations and have enabled a better understanding of how external factors affect gait. Split-belt treadmills, a special treadmill system that provides a way to force both legs to walk at different speeds, have provided a variety of knowledge on how individuals adapt to external perturbations. For example, presentation of visual or tactile feedback can enable greater ability to adapt to split-belt perturbations [159, 112]. Implicit motor adaptation patterns that minimize step length asymmetries also seem to supersede explicitly provided gait strategies in a split-belt paradigm [103], although provision of explicit strategy instructions on a tied belt (regular) treadmill does lead individuals to change gait strategies [121]. Given that TCLE systems provide external perturbations, understanding how different perturbations impact gait strategy can enable safer and more effective TCLE system use.

Walking speed is another essential factor that can impact individuals gait characteristics and overarching strategy and therefore must be considered when investigating human gait and task performance with TCLE systems. It is well known that characteristics such as stride length and stride time change with different walking speeds [66]. Walking speed also impacts how well someone adapts to external perturbations and completes secondary tasks while operating a TCLE system. For example, when completing split-belt walking at varying speeds, individuals were found to have greater after-effects, (a sign of longer- lasting motor adaptation) at slower speeds relative to faster speeds [194]. An in-depth discussion of walking while dual tasking can be found in Section 1.4.1. Examining how gait strategies and task performance change at varying walking speeds is essential to exoskeleton

24 use in operational environments, especially in the context of rehabilitation or exoskeleton use for medical indications. Walking speed is sometimes considered the ‘sixth vital sign’ in clinical biomechanics [120] and can provide important information about an individual’s functional status. Walking speed has been used to classify individual’s functional ability post-stroke [20, 141]. These same populations would greatly benefit from exoskeleton use for gait restoration or rehabilitation, thus an understanding of how gait strategy and task performance with TCLE systems is impacted by varying walking speeds could inform clinical indications of exoskeleton or other TCLE system use.

1.4.1 Gait Under Divided Attention

Use of exoskeletons in operational environments will likely require the ability to complete secondary tasks while operating the system, e.g. paying attention and appropriately responding to traffic and crosswalk signals, holding a conversation while walking, manipulating aphone or other handheld device, etc. This dual task requirement adds an important attentional component to exoskeleton operation - individuals must have the ability to selectively direct attention to cues from their environment, cognitively process those cues, and respond accordingly, all while continuing to ambulate with the system. Dual task studies with gait as the primary task, used to assess cognitive resources required to walk in the presence of a variety of secondary tasks, provide a mixed view of how attention, gait, and secondary tasks interact. Task prioritization changes dual task performance. For example, healthy adults, both young and old, seemed to prioritize motor tasks during a gait dual task paradigm and made more cognitive errors, while patients with Parkinson’s disease did not prioritize gait or secondary cognitive tasks more highly, rather patients showed decrements in both gait and cognitive task performance [17]. Secondary task type also impacts gait, but in a variety ways. Secondary tasks that are more cognitively demanding, e.g. those that require working memory or verbal fluency, seem to detrimentally impact gait (i.e. decreased speed, cadence, stride length, and increased stride time) more so than relatively simple visual reaction time tasks [1]. However, reaction time tasks can lead to decreased gait speeds, as seen in both older and younger adults during a visual reaction time task [181]. Differences in gait modulation vary depending on the subject’s age [16]and health status [170, 124] as well, making it difficult to provide an easy answer to the question of how gait, attention, and secondary task completion are related.

25 Importantly, there are limited studies that explore the effect of tactile secondary stimuli in particular on gait or dual task performance. Because TCLE systems provide tactile cues, this is am important question to consider. Just as the impact of tactile cues on cognitive fit is unknown, effect of tactile cues on attention during ambulation is unclear andrequires further study. This thesis explores how tactile cues impact attention in a ambulatory dual task paradigm in the context of TCLE system operation. Finally, as mentioned previously, gait speed also interacts with individuals’ ability to dual task. Dual tasking generally leads to decreased walking speed [1], indicating that walking at different speeds requires varying amounts of cognitive resources and attention. How walking speed impacts TCLE operation is an open question. Spinal cord injury patients’ ambulation speed was related to their hip, knee, and joint angle profiles during exoskeleton use [189], indicating some relationship between walking speed and TCLE system operation. However, there is no information regarding how different walking speeds impact TCLE system operation in healthy individuals, providing an opportunity to explore cognitive resource use and attention in the context of varying walking speeds and TCLE system operation.

1.4.2 Exoskeleton-Augmented Gait

A number of gait characteristics change during exoskeleton operation, but healthy individuals may modulate their own exoskeleton-augmented gait in distinct ways. A number of exoskeleton adaptation studies have shown that gait characteristics like muscle activation patterns and joint angle profiles move suddenly away from baseline patterns when the exoskeleton is powered on, then slowly reach a new steady state after some time [65, 90]. Generally these changes in exoskeleton gait characteristics occur on the order of tens of minutes, although specific length of time and quality of adaptation to an exoskeleton can depend on factors like the exoskeleton control method [24]. Other studies have observed decreases to individuals’ metabolic cost of walking [61, 122, 165] when assisted by lower-extremity exoskeletons. However, these gait changes for a given exoskeleton are not always found to be consistent across users. In one case, not all subjects were found to achieve a new steady state after 30 minutes of walking with a powered ankle exoskeleton [90]. In another case, investigators observed both metabolic cost increases and decreases across different individuals during a first exposure to the system [165]. Metabolic cost is most often used to evaluate exoskeleton effectiveness and serves to pro-

26 Figure 1-5: An example of a potentially metabolically costly gait strategy while using a powered ankle exoskeleton that provides added push-off power. This added powered has the result of propelling the foot forward, but individuals may fight that added power by activating muscles required to place their foot on the ground sooner than necessary or optimal. vide a useful metric by which to compare across different exoskeletons systems. A number of exoskeletons have achieved the goal of reducing users’ metabolic cost during exoskeleton operation relative to gait without the system [164], including both active and passive systems (e.g. [122, 61, 165]). However, metabolic cost reductions do not provide information regarding what gait strategies individuals might be employing that result in those metabolic changes. An increase in metabolic cost may be seen as a failure of the exoskeleton to improve that individuals’ walking or running economy. However, it may be that the person was employing a specific gait strategy that resulted in increased metabolic cost, rather thana feature of the system itself. For example, if a person decreases their stride length while using a powered ankle exoskeleton that adds power during toe-off (when the foot pushes off from the ground), they may increase metabolic cost because they are fighting the added power from the system to plant their foot sooner (Figure 1-5). In this case, increased metabolic cost is not a failure of the device, rather a misunderstanding or misuse of the system by the human operator. Understanding these nuances in exoskeleton operation can improve exoskeleton design and training for operational use cases. While walking, individuals must weigh a number of interacting gait elements including

27 metabolic efficiency, stability, coordination, and fall risk. Any number of varied strategies may optimize for different components of gait in distinct contexts. These same calculations occur during exoskeleton-augmented gait and result in the gait characteristics observed during exoskeleton gait and adaptation studies. A goal of this thesis is to investigate the specific gait characteristics individuals are utilizing that result in the changes observed during exoskeleton operation, as well as individual differences in exoskeleton gait strategy and adaptation more broadly.

1.5 Thesis Objectives and Aims

This chapter has provided background on and discussed a number of cognitive functions that may be related to exoskeleton gait characteristics and enable good cognitive fit of systems with users. Inhibitory control, an essential aspect of executive function that enables individuals to ignore irrelevant cues or inhibit incorrect responses [41], may provide information on how individuals respond to a system that provides unfamiliar tactile cues or inhibit reactions that could result in inappropriate system operation, e.g. increased fall risk, during TCLE system use. Ability to complete tasks under divided attention is also increasingly relevant to TCLE systems, given the goal of enabling functional usage of exoskeletons in real-life scenarios [185]. Prior to exploring relationships between exoskeleton gait characteristics and inhibitory control and attention, it is essential to step back and understand the effect of alternative interaction modalities on system usage and task performance. TCLE systems by design provide tactile cues and require lower-extremity responses, a cue-response pair not commonly implemented in current, widely-used devices. Thus, we must first lay a foundation to understand the impact of these alternative interaction modalities on human task performance, which we can then build upon to further characterize human interaction and gait strategy with TCLE systems. The overall objectives of this thesis are to provide a deeper understanding of how individuals walk and interact with TCLE systems and how the new modes of interaction presented by such systems impact goal-oriented task performance. Ultimately, this knowledge can be used to improve system design and training methodologies from a human factors perspective, enabling easier and more effective use of such systems. We further aim to provide abasisof

28 what cognitive factors may underlie individualized variation in exoskeleton gait strategies. Thus this thesis revolves around three main research questions:

1. How do alternative interaction modalities impact peoples’ ability to complete goal- oriented tasks with human-machine systems?

2. How do tightly-coupled lower-extremity systems impact individuals’ gait strategy and ability to perform goal-oriented tasks?

3. What cognitive factors underlie individualized variation in gait strategy with lower- extremity exoskeletons?

To address these questions, this thesis has the following specific aims:

1. Assess the effect of varying interaction modalities on a reaction time task.

2. Characterize human gait strategy while walking on a self-paced treadmill system with a dual task.

3. Evaluate the effects of a powered lower-extremity exoskeleton on human gait strategy.

4. Identify factors that may be related individualized human gait strategy during exoskeleton use.

Chapter 2 will address Aim 1 by investigating the effect of alternative cue-response pairs on a reaction time task. Chapter 3 will describe a study protocol designed to assess Aims 2-4, which consists of a series of baseline perceptual, motor, and cognitive tasks followed by bouts of walking with a lower-extremity exoskeleton. Details only of the cognitive tasks and one exoskeleton walking protocol are presented because this thesis focuses only on those data. Chapter 4 will address Aim 2 by exploring the overall effect of speed and dual task on gait strategy and task performance with a particular TCLE system, the self-paced treadmill. Chapter 5 will present individualized exoskeleton gait strategy characteristics to address Aim 3. Finally, Chapter 6 will combine individuals’ data from all cognitive baseline tasks in Chapter 3 with their exoskeleton gait characteristics to address Aim 4 of the thesis and assess what baseline cognitive factors may underlie individuals’ exoskeleton gait strategy.

29 30 Chapter 2

Human Cognition & Performance in Alternative Interaction Modes

TCLE systems by design entail novel interaction modalities between the system and the user. They require motor actions and reactions to be completed using the lower extremities. While walking is commonplace amongst most healthy individuals, people do not generally utilize their lower limbs to respond to cues, rather arm and hand manipulations are far more common when interacting with the environment. Furthermore, TCLE systems, in particular wearable systems such as exoskeletons, provide tactile cues to the operator that must be understood and acknowledged. While visual and auditory cues are common in everyday scenarios, tactile cues are less so. Thus, in order to assess human interaction with TCLE systems, we must first explore how individuals react to and perform tasks in the presence of alternative cue and response modalities.

This chapter addresses Aim 1 of the thesis, specifically investigating the effect of alternative cue-response interaction modalities on task performance in the context of an inhibitory control probe, the Simon task. The Simon task probes individuals’ ability to ignore irrelevant or distracting environmental stimuli and/or inhibit inappropriate responses [41] and is a physical manifestation of the Simon effect, the phenomenon that reaction times are increased when cues are presented in a spatially incongruent manner, leading to task interference [172]. This chapter provides background on inhibitory control and the Simon task, as well as the importance of alternative interaction modalities in emerging technologies (i.e. exoskeletons and immersive virtual reality systems). This chapter then presents methods

31 and results of a study that extends the Simon task to alternative cue-response pairs to assess whether the Simon effect is maintained across alternative interaction modalities, as well how these modalities impact task performance (i.e. response accuracy and response rate). The chapter concludes with a discussion on the impact of alternative interaction modalities on task performance and how this information can be utilized to inform the design of emerging technological systems.

2.1 Executive Function and the Simon Effect

Executive Functions (EF) are a set of cognitive processes that include the mental control and flexibility to perform complex tasks, including novel technology interactions. Inhibitory control is one of these cognitive processes, which can be broken down into response and attentional inhibition [41]. Response inhibition enables behaviors that are not immediately obvious or that might contradict an instinctual response. For example, when a driver feels their car hydroplaning, often the instinctual response is to press down on the brakes. How- ever, the proper response is to let off the brake to allow the wheels to regain sliding friction with the road surface. Similarly, attentional inhibition allows for selective sensory filtering in complex environments, which can present irrelevant and distracting stimuli. Consider the example of landing a plane, where multi-modal stimuli are simultaneously processed by the pilot. These stimuli include visual cues from the control panels and through the cockpit windows; auditory information from the co-pilot, ground control, and warning systems; and haptic feedback from the control interfaces. The pilot must continually assess which cues are relevant and necessary while ignoring any irrelevant and interfering stimuli. In both cases, inhibitory control is essential for maintaining safe vehicle operation.

2.1.1 The Simon Task

While it is not possible to directly measure inhibitory control, tasks have been designed to illustrate the effects of this cognitive process. The Simon task is one method usedto evaluate inhibitory control indirectly by measuring reaction times to stimuli presented with and without irrelevant cue information. In the original presentation [172], auditory cues were presented that required either left- or right-sided response with a finger button press. When the cues were presented in a spatially incongruent manner (e.g., a right-sided audio

32 cue presented to the left ear), reaction times were greater than those for congruent trials (e.g., a right-sided audio cue present to the right ear). This response time difference in the presence of spatially incongruent cue presentation is classified as a Stimulus-Response (S-R) compatibility effect [58] specifically termed the Simon effect.

2.2 Stimulus-Response Compatibility and System Design

Stimulus-Response compatibility in an important consideration in human factors research and system design. Spatial S-R compatibility plays a key role in enabling efficient human task performance when interacting with technological systems [99, 151]. For example, placing indicator lights on a car medially to the headlights (i.e. a left turn indicator placed on the right side of the left headlight) results in slower, less accurate responses [11]. Compatible S-R mappings enable better interaction with technological systems than incompatible mappings, such as when designing inputs to systems with multiple displays [126].

2.2.1 Effect of Alternative Interaction Modalities

It is important to consider how S-R compatibility effects may be impacted by alternative interaction modalities, especially in the context of system design. The Simon task has been demonstrated using auditory or visual cues [172, 171, 173] and two categories of response modalities: hand/arm manipulations or vocal response [105]. Since then, a new era of technologies that includes immersive virtual environments (IVEs) and wearable robotics has driven technology developers to adopt alternative cue and response modalities. Haptic cueing, specifically, has been a pivotal sensory feedback modality that has provided operator immersion and improved human-system performance [136, 149, 169]. Haptic technologies have also been studied as a means to provide complex information to a human operator, e.g. surgical simulators [188], aviation displays [177], and VR training systems [187]. Further, haptic feedback arises naturally from systems like wearable robotics. Similar to new cueing modalities, response modalities have expanded with the development of new technologies such as lower limb exoskeletons, which necessitate tightly-coupled lower-extremity movement coordination. There is currently a gap in our knowledge of the compatibility effects of these new technology interface modalities compared to traditional upper extremity responses to vi-

33 sual/auditory cueing. The full perception-action loop for response to stimuli incorporates the sensory conduction time for the cue to reach the brain, Tsen; the cognitive processing time, Tcp, including any and all internal neuronal connections required to interpret the cue and select a response [152]; and motor conduction time for signals to reach the end effec- tor from the brain, Tmot. Previous literature would suggest that haptic cues and lower extremity responses may result in slower response times and reduced accuracy. For example, increasing the complexity of the movement (e.g., moving the leg rather than a finger) increases Tmot due to more muscle recruitment and greater degrees-of-freedom, thereby increasing response time [74]. Muscle stiffening, movement, or changes in posture can lead to differences insen T [44], meaning tactile cues in an operational environment may increase

RT or reduce accuracy. Lower-extremity responses and tactile cues may also increase Tcp via more extensive integration of neuromotor coordination patterns or different neuronal cue interpretation pathways, respectively. Therefore, differences in modalities and locations chosen for a cue-response pair may affect S-R compatibility. In this study, we investigated whether the Simon effect is conserved across a series of alternative cue-response pairs. Subjects were presented congruent and incongruent visual or vibrotactile cues and responded by making upper extremity (i.e., finger press) or lower extremity (i.e., foot-tap) movements. We hypothesized (i) an increased response time for incongruent stimuli in each of the experimental conditions (Simon effect) and (ii) effects of cue and response modality on reaction time, accuracy, and response rate (RR). We also present an exploratory decomposition of the response time to estimated cognitive processing time across modalities.

2.3 Materials and Methods

2.3.1 Participants

Twenty healthy adults (10 male, 10 female) aged 23- 43 years (mean +/- SD, 27.85 +/- 5.75 years) participated in this study. This research complied with the American Psychological Association Code of Ethics and was approved by the MIT Committee on the Use of Humans as Experimental Subjects (COUHES). All participants provided written informed consent. Subjects were excluded if they reported any lower or upper extremity injuries in the past six months that may have impeded adequate protocol completion.

34 2.3.2 Experimental Protocol

The experiment protocol was conducted in the High-end Computer Assisted Rehabilitation Environment (CAREN) System (Motekforce Link, Netherlands) at the MIT Lincoln Labo- ratory Sensorimotor Technologies Realization in Immersive Virtual Environments (STRIVE) Center. Throughout each experiment session, the D-Flow software (Motekforce Link, Nether- lands) controlled the CAREN System and synchronized data acquisition.

Subjects stood in the center of the virtual reality dome holding an Xbox game controller (Microsoft Corporation, Redmond, WA) with each foot on an independent load cell (Motekforce Link, Amsterdam, Netherlands) and wearing four vibrotactile devices. These devices were constructed from Bluetooth-enabled, battery-powered boards (MbientLab Inc., San Francisco, CA), integrated with mini coin vibrating motors (approx. 183 Hz) (Adafruit Industries, New York, NY), and housed in a 3D printed plastic case. The four vibrotactile devices were placed in custom fabric holders with separate pockets for the board/battery case and the coin motor. The devices were attached to the medial and lateral sides of each of the subjects’ upper legs with touch fastener straps. The fabric holder positions on the straps were independently adjustable to account for differences in subject anthropometry. Motor positions were shifted within the lateral and medial regions prior to experimentation such that participants reported being able to perceive and localize vibrations from all four motors.

The protocol consisted of four variations of the Simon Task. Each variation was a combination of either a visual (V) or tactile (T) cue and an upper (U) or lower (L) extremity response. The congruent and incongruent visual cues were projected on a large screen in front of the subject and tactile cues were provided via the custom vibrotactile devices on the upper leg. Cue and response pairs are visualized in (Figure 2-1. Upper extremity responses were recorded as left or right button presses on an Xbox game controller. Lower extremity movement responses were recorded as left or right foot-taps.

To control for learning and fatigue effects, five subjects were each assigned to one of four condition orders (Order 1: VU – VL – TU – TL, Order 2: TL – TU – VL – VU, Order 3: VU – TU – VL – TL, Order 4: TL – VL – TU – VU). Orders were chosen such that all visual, tactile, upper, or lower conditions were completed in the first two trials, with the assumption that if no effect of order was observed, a learning effect was not supported.

35 Figure 2-1: A. Four treatment conditions were each unique combination of cue-response pair: visual cue/upper extremity response (abbreviated as VU), visual cue/lower extremity response (VL), tactile cue/upper extremity response (TU), and tactile cue/lower extremity response (TL). B. Congruent and incongruent visual cues were presented as arrows on the screen in front of participants on either the left or right side of the fixation point (crosshair at the center of the screen). Baseline (no-go) cues were presented as two horizontal bars in the place of the arrows. C. Tactile cues were presented using vibrotactile motors placed around participants’ thighs. Vibration locations were mapped to left/right congruent/incongruent cues as shown. Baseline (no-go) cues were defined as all four motor vibrating at thesame time. Pictured as though participants are facing away.

Within each condition, 100 individual stimuli were presented: 40 congruent stimuli (20 right-sided response, 20 left-sided response); 40 incongruent stimuli (20 right-sided response, 20 left-sided response); and 20 baseline stimuli requiring no response. Successive stimuli were presented 1-1.25 seconds after the previous stimulus ended. For each of the four conditions, the same random pattern of stimuli and inter-stimuli interval timings were presented to all subjects.

2.3.3 Data Analysis

Reaction time (RT) for all trials was calculated as the time from cue presentation to exe- cution of a movement response. Visual and tactile cue presentation and upper extremity

36 response times were recorded directly in D-Flow. Lower extremity response times (i.e., foot- tap) were measured from each of the left and right force plates and defined as the first time at which the force magnitude measured below zero Newtons after the stimulus cue, signifying foot clearance from the ground. For all trials, only the first response following cue presentation was considered in RT calculations. Each response was additionally given two binary ratings for accuracy (correct/incorrect) and response (response/no response, regardless of accuracy). All analyses were performed in MATLAB (Mathworks, Natick, MA).

To evaluate effect of cue type (V/T), response type (U/L), subject, condition order, and stimulus congruency (congruent or incongruent spatial presentation), a repeated measures Analysis of Variance (ANOVA) was fit for mean RT with those five factors. Significance level was set at p<0.05. Subject was treated as a random factor, all other factors were considered fixed, with subject nested in condition order. Baseline trials (no-go trials), incorrect trials, and missed response trials (trials which required a response, but the subject did not respond) were excluded from the RT model so that it only included correct responses with defined RT. The initial model supported no main or interaction effect of order, so that factor was removed and the model refit. To understand effect of alternative interaction modalities on task performance, post-hoc pairwise t-tests were conducted on mean RT for each cue- response pair with pooled congruent and incongruent cues. Effect size was calculated using Cohen’s d with the following interpretations: 0.2 as small, 0.05 as medium, and 0.8 as large [166].

Further analysis was conducted to understand the impact of cue-response pair on Simon task performance as measured by accuracy and response rate (RR). Baseline trials and stimulus congruency factor were not considered in these analyses. Mean accuracy and RR were calculated for each subject per cue-response pair as the proportion of stimuli with a correct response or any response, respectively, out of 80 total stimuli. To assess differences in variance of accuracy and RR across the four cue-response pairs (treated as a single factor with four levels: VU, VL, TU, and TL) two Bartlett’s tests (one on accuracy, one on RR) were conducted, followed by post-hoc pairwise comparisons using two-sample F-tests. To assess differences in mean accuracy and RR across cue-response pairs, two Friedman tests (one on accuracy, one on RR) and post-hoc Wilcoxon sign rank tests were conducted.

To understand the impact of alternative cue-response pairs on cognitive processing time, two exploratory analyses were conducted. First, difference in mean RT for congruent and

37 incongruent stimuli was calculated for all subjects per cue-response pair (i.e. mean RT for congruent stimuli was subtracted from mean RT for incongruent stimuli, resulting in four data points per subject, one for each cue-response pair). A one-way ANOVA model was fit for a dependent variable of mean difference in RT with a single factor of cue-response pair. Second, cognitive processing time (Tcp) was estimated by subtracting sensory and motor conduction time values from observed RTs. Evoked potential (EP) literature was reviewed to find estimates of sensory and motor conduction times. Visual cue conduction time was estimated as 0.095 sec from visual EPs [2, 179]. Tactile cue conduction time was estimated to be 0.035 sec. from somatosensory EPs at the popliteal nerve [54, 63]. Motor signal travel time for upper (measured at the hand) and lower (measured at the biceps surae or tibialis anterior) extremity responses were estimated to be 0.020 sec [29, 87] and 0.030 sec [29, 87, 42], respectively. Variations in conduction times due to factors such as age, leg length, or BMI were not considered in this exploratory analysis. Paired t-tests with data pooled across stimulus congruency factor were used to evaluate effect of cue-response pairs on estimated cognitive processing time.

2.4 Results

2.4.1 Response Times

The data support maintenance of the Simon effect across alternative cue-response pairs, indicated by a significant effect of congruency in the ANOVA model (Table 2.1). Allfour cue-response pairs showed significant increases in mean RT for incongruent trials relative to congruent trials (Figure 2-2). The data also support significantly different task performance as a result of alternative interaction modalities across participants. Results of the ANOVA model on RT (Table 2.1) specifically support significant main effects of subject, cue, and response. A significant interaction between subject and cue had a small effect size. Interactions were driven bya difference in magnitude, with all subjects showing increased RT with tactile cues ascompared to visual cues. All other interactions were found to be either not significant or had a negligible effect size휂 ( 2< 0.01). Post-hoc paired t-tests also showed significant differences in RT across cue-response pairs (p < 0.05). Specifically, tactile cues and lower-extremity responses led to increased RT.

38 Figure 2-2: Response times for each cue-response pair split by congruency. RT were significantly different (p < 0.05) between each cue-response pair when pooled for congruency (differences not notated symbolically in figure). RT were also significantly faster (p<0.05) for congruent stimuli than incongruent stimuli within each cue-response pair (notated with an asterisk).

39 Table 2.1: Output showing F-values, p-values, 휂2 values, and effect sizes for an ANOVA model on RT with four factors: subject, cue mode, response mode, and congruency. Significance at an alpha value of 0.05 is indicated by an asterisk in thep-value column. 휂2 values and effect sizes only shown when significance criteria were met.

Factor F-value p-value 휂2 Effect Size Subject 3.1705 0.0040* 0.1093 M Cue 297.8978 <0.0001 * 0.3673 L Response 285.3277 <0.0001 * 0.2506 L Congruency 54.6588 <0.0001 * 0.0130 S Subject x Cue 2.6381 0.0150* 0.0235 S Subject x Response 2.0463 0.0616 N/A N/A Subject x Congruency 1.8515 0.1155 N/A N/A Cue x Response 17.5519 0.0005* 0.0072 Negligible Cue x Congruency 8.5875 0.0086* 0.0010 Negligible Response x Congruency 7.3515 0.0138* 0.0005 Negligible Subject x Cue x Response 7.3423 <0.0001 * 0.0079 Negligible Subject x Cue x Congruency 1.9805 0.0727 N/A N/A Subject x Response x Congruency 1.3004 0.2863 N/A N/A Cue x Response x Congruency 0.6841 0.4184 N/A N/A Subject x Cue x Response x Congruency 1.5138 0.0703 N/A N/A

2.4.2 Accuracy and Response Rate

Alternative cue-response pairs lead to differences in task performance as supported by Bartlett’s tests indicating significant difference in variance of accuracy and response rate (RR) across cue-response pairs. Lower-extremity responses lead to greater variation in accuracy as shown by results of post-hoc two-sample F-tests. Variances of VU/TU were significantly less than variances of VL/TL (p < 0.05, VU/TU and VL/TL pairs werenot significantly different). Two-sample F-tests on RR data showed that all pairwise variances were significantly different (p < 0.05) except for VU-TU.

Figure 3 displays median accuracy and RR data for each cue-response pair and significant differences resulting from post-hoc Wilcoxon sign-rank tests. Friedman tests supporta significant effect of cue-response pair on median accuracy (휒2(3) = 13.08, p = 0.0045) and median RR (휒2(3) = 10.45, p = 0.0151). These results further show that alternative cue- response modalities impact task performance, with median accuracy decreased for lower extremity responses and RR lower for tactile cues. Table 2.2 enumerates z, p, and r values, as well as effect sizes resulting from post-hoc Wilcoxon sign-rank tests on accuracy andRR.

40 Figure 2-3: Median accuracy and response rate (RR) across all subjects by cue-response pair. Asterisks denote significantly different pairwise comparisons at an alpha level ofp< 0.05. Statistical values for Wilcoxon sign-rank tests and effect sizes can be found in table 2.

2.4.3 Cognitive Processing Time

Cue-response pairs may impact cognitive processing time. Observed differences in mean RT between congruent and incongruent cues were found to be significantly different for cue-response pairs (F(3,76) = 4.25, p = 0.008). Tukey post-hoc tests revealed that mean RT difference was greater in the TL condition than VU (p = 0.004). All other paired comparisons were not significant (p > 0.05). Increased RT with different cue-response pairs may not be due solely to varying sensory and motor signal conduction times. Estimated cognitive processing times, Tcp, for each cue- response pair (Table 2.3) were significantly different from every other cue-response pair (all six paired t-tests resulted in p < 0.001), which indicates that tactile cues or lower-extremity responses may increase the time necessary to cognitively process and respond to cues.

Table 2.2: Z-values, p-values, r values, and effect sizes for post-hoc pairwise Wilcoxon sign- rank tests on accuracy and response rate (RR). Significance at an alpha value of 0.05 is indicated by an asterisk in the p-value column. Values for r and effect sizes only shown when significance criteria were met. Boxplots of this data are presented in Figure2-3.

Accuracy Response rate (RR) C-R Pair Z p r Effect Size z p r Effect Size VU – VL 2.485 0.013* 0.556 L 0.986 0.324 N/A N/A VU – TU 2.504 0.012* 0.560 L 1.828 0.068 N/A N/A VU – TL 3.027 0.003* 0.677 L 2.065 0.039* 0.462 M VL – TU 1.004 0.316 N/A N/A 2.278 0.023* 0.509 L VL – TL 1.112 0.266 N/A N/A 2.383 0.017* 0.533 L TU – TL 2.372 0.018* 0.530 L 0.881 0.378 N/A N/A

41 Table 2.3: Cognitive processing time values calculated from mean RTs per subject and estimated values of total signal travel time for sensory and motor signals for each cue- response pair. Mean and standard deviation of observed RT for each cue-response pair are presented for reference. Estimated Tcp for each cue-response pair was significantly different from all other cue-response pairs.

Tsen + Tmot Observed RT Estimated Tcp VU 0.115 sec 0.507 ± 0.114 sec 0.392 ± 0.114 sec VL 0.125 sec 0.724 ± 0.126 sec 0.599 ± 0.126 sec TU 0.055 sec 0.780 ± 0.169 sec 0.723 ± 0.169 sec TL 0.065 sec 1.080 ± 0.229 sec 1.015 ± 0.229 sec

2.5 Discussion

This study evaluated the generalizability of the Simon effect when using alternate cue- response pairs beyond the traditional visual/auditory cueing with a hand/arm manipulation response. For the healthy adult population recruited in this study, the Simon effect (greater reaction time for incongruent cues) was conserved for all tested cue-response pairs. The following discussion expands on how the choice of modality for both cue and response influenced the average RT, magnitude of the Simon effect, task accuracy, response rate,and cognitive processing time.

The results of this study support the hypothesis that the Simon effect is maintained across alternative cue-response pairs. RT for incongruent stimuli were greater than RT for congruent stimuli across all cue-response pairs, indicating that inhibitory control can be observed by implementing the Simon task with modalities beyond traditional visual/auditory cueing and finger/hand manipulation response. The difference in RT due to congruency within the VU condition in the present study was 28 msec, which is consistent with other Simon task studies implemented with various types of visual cues, i.e. colors, shapes, and letters, and button press responses [73, 130, 150]. Extending the Simon task to alternative interaction modalities will enable understanding of stimulus-response compatibility in the context of novel technologies which enable new modalities of human-system interaction, e.g. exoskeletons which provide tactile cues and may require lower-extremity responses, or haptic inputs with upper extremity responses for augmented reality interfaces. However, each factor of cue and response had unique impacts on RT. Therefore, while the Simon effect is conserved, it is necessary to consider the impact cue-response pairs mayhaveonRT when designing or evaluating technology interfaces with alternative interaction modalities.

42 Visual cues are more intuitive than tactile cues, as observed by faster reaction times, greater accuracy, and higher response rates (RR). Tactile cues can be limited because individuals have varying sensory perception thresholds, which can further change as a result of muscle stiffening, movement, or changes in posture leading to differences in sensory sig- naling [44]. Depending on the design of the tactile cue, shifting placement on the body can also lead to varying sensory perception [113, 125]. Such concerns can make tactile cues less robust than visual cues in certain scenarios. Differences in perception were observed during this study, with some participants initially reporting difficulty in accurately perceiving or lo- calizing vibrations during testing. However, intentional cue design can help to mitigate such concerns, especially in cases where tactile cues are relevant or necessary (e.g. exoskeletons or immersive VR systems). For example, motor positions were adjusted individually for all participants to enable accurate perception in the present study. Specific design choices can also increase the robustness of tactile cues. Continuous tactile cues may suffer from perception losses due to habituation [22, 23]. While intermittent vibratory cues were used in this study to minimize effects of habituation, decreased RR for tactile cues indicate thatsome habituation may still have occurred. Design choices such as stronger motors or vibrotactile systems which stimulate slightly different mechanoreceptors at different times could beim- plemented to further overcome habituation effects. Tactile cues were placed on the lower extremities in the present study, enabling the present results to inform stimulus-response compatibility in gait-assisting or augmenting exoskeletons. While visual cues may be more intuitive in general, intentional and informed design decisions can make tactile cues a useful tool in technology and system design.

The upper extremities enable better task performance in the Simon task than lower extremities, as quantified by faster RTs, higher median accuracy, and lower variability in accuracy. These results align with more common use of the upper extremity to perform fine manipulations and activities of daily living. Design principles specifically recommend that tasks demanding high levels of accuracy be designed for the upper extremities [33]. Further, individuals are accustomed to manipulating their upper extremities in response to visual cues to accomplish many complex daily tasks, e.g., manipulating objects or interfacing with systems like a computer, phone, tablet, etc. However, the lower extremities are commonly used in tasks which do not require such high accuracy or precision, e.g., foot pedals in a car or bike. Furthermore, sub-second responses may not be necessary in tasks involving gait

43 or select tasks in immersive VR environments. Lower extremity responses are necessary by design in systems like ankle, knee, or leg exoskeletons. When very accurate, precise, or fast responses are required with systems like lower-extremity exoskeletons, training can be used to improve performance, though further work should explore the efficacy of different training methods.

Greater variance in accuracy of lower-extremity responses relative to upper-extremity responses may indicate that individuals adopted more varied strategies while responding with their lower extremities. For example, when responding with the lower extremity, some participants minimally shifted their weight, while others visibly shifted their bodies towards the contralateral side when lifting the response foot. Such varying behaviors may also be emergent in new wearable or immersive systems. Evaluation of human interaction with these systems should consider how different strategies may impact task performance. While performance trade-offs, such as those between accuracy, precision, and speed are well doc- umented broadly, e.g. Fitt’s law [59], and specifically in human movement behaviors such as grasping tasks [19], and aimed hand and foot manipulations [190, 201], to our knowledge there is limited research exploring the trade-off in accuracy and RT when completing specific tasks with the lower extremity. There can be large differences in timescale between grasping and walking tasks, i.e. on the order of milliseconds and seconds, respectively, and these timescale differences can result in differential task performance with the upper versus lower extremities. Further research in trade-offs of accuracy and RT specifically with the lower extremity can inform design criteria for embedded controllers within wearable technologies.

Alternative cue-response pathways may impact cognitive processing time (Tcp) in addition to the sensory and motor components of the perception-action loop. If increased RT across cue-response pairs was due solely to changes in sensorimotor conduction times and not changes to Tcp, the difference in RT due to congruency (i.e. mean incongruent RT–mean congruent RT, the observable aspect of inhibitory control) would be expected to remain constant across cue-response pairs. However, present data supports greater difference in RT due to congruency in the TL condition than VU, indicating that varying cue and response modalities impact Tcp in addition to the sensorimotor components of the perception-action loop. Additionally, estimated values for Tcp, calculated by subtracting sensorimotor conduction times from observed RT, were also different across cue-response pairs. Variation in sensorimotor conduction times due to age, sex, and height [2] were not considered in

44 this analysis. However, variation in conduction times due to such endogenous factors is on the order of tens of milliseconds or less [6, 106, 161]. Therefore, unaccounted changes in conduction time due to endogenous factors does not account for the differences in estimated

Tcp observed across cue-response pairs, which were greater than 100 msec for all pairwise comparisons. The present analysis also does not consider the possibility of anticipation or prediction in action control [30, 92]. In the case that anticipation or prediction do play a role in the perception-action loop, the present values provide an upper bound for Tcp, although anticipation would be minimized in the present study through the randomization of cue presentation. Technology design should additionally consider the impact of device location on the body and gating (blocking of sensory signals below certain thresholds) due to movement [162] on sensorimotor conduction. While further investigation on neurological and cognitive changes due to varying cue-response pairs is necessary, the present data provide preliminary evidence that cognitive processing is impacted by alternative interaction modalities.

2.6 Conclusion

Inhibitory control, the ability to inhibit impulsive responses and irrelevant stimuli, enables high level functioning and activities of daily living. Inhibitory control is essential to utilizing new technologies which make use of alternative interaction modalities, e.g., lower- extremity exoskeletons or immersive virtual reality systems with haptic feedback. Here we provide evidence that the Simon task, which illustrates inhibitory control, is maintained across alternative interaction modalities, namely different cue (visual/tactile) and response (upper/lower extremity) pairs. However, alternative cue-response modalities impact Simon task performance as observed by slower response times, lower accuracy and response rates. These performance decrements may be due to changes in cognitive processing, not solely changes in the sensorimotor conduction pathways. Therefore, it is essential to understand the effect of differing cue and response modalities on task performance and cognitive processes like inhibitory control when designing and evaluating technologies that enable new modes of human-system interaction.

The maintenance of the Simon effect with alternative cue-response pairs further provides a method by which to probe individuals’ inhibitory control using interaction modalities relevant to TCLE systems. In particular, we use these study results to design a modified

45 Simon task, implemented with tactile cue presentation and lower-extremity response, as a new method of probing inhibitory control that maintains the original Simon effect. The modified Simon task will be used in this thesis to probe individuals’ inhibitory controlasa candidate cognitive factor that may be correlated to or predictive of individualized variation in exoskeleton gait characteristics.

46 Chapter 3

The Human-Exoskeleton Strategy & Adaptation Study

Having shown that alternative interaction modalities can impact task performance on a reaction time task, we now move to to addressing Aims 2-4 of this thesis. These three aims are closely related. Aims 2 and 3 focus on assessing human interaction and gait strategy with two tightly-coupled lower extremity systems: self-paced treadmills and lower-extremity exoskeletons. Aim 4, which is to identify factors that may be related to individualized human gait strategy with an exoskeleton, utilizes and compares data from both Aims 2 and 3.

Individuals’ ability to complete secondary tasks on a self-paced treadmill provide measures of task performance under divided attention. The modified Simon task, as presented in Chapter 2, provides another probe of cognitive function, specifically inhibitory control. Both attention and inhibitory control may be predictive of individualized variation in exoskeleton gait. Thus, it was desired that the same individuals complete the modified Simon task, operate the self-paced treadmill, and walk with a lower-extremity exoskeleton to enable comparison of cognitive factors and exoskeleton gait strategies within individuals. The Human-Exoskeleton Strategy & Adaptation (HESA) study was developed to enable these within-subject comparisons. The HESA study consisted of a two-day study protocol that incorporated the aforementioned tasks, as well as some additional measures of individuals’ sensory and motor function. This chapter provides an overview of the cognitive baseline tasks and exoskeleton walking protocol presented in this thesis. Details of tasks in the HESA protocol not explored in this thesis can be found in Appendix B.

47 3.1 Protocol Overview

Here we present a two-day study protocol (Figure 3-1) developed to quantify individuals’ baseline sensorimotor and cognitive abilities and subsequently explore relationships between those baseline abilities and individuals’ gait characteristics with a powered ankle exoskeleton. A number of perceptual, cognitive, and motor tasks were selected to evaluate a variety of baseline factors. Here we present only the cognitive baseline tasks. Individuals completed three cognitive tasks - a modified Simon task, a speed achievement task on a self-paced treadmill, termed self-pacing (SP), and a self-paced dual task (SPDT). The modified Simon task aimed to assess individuals’ inhibitory control using interaction modalities most relevant to operation of TCLE systems, namely with tactile cues and lower- extremity responses. The speed achievement task on the self-paced treadmill (SPT) was to assess baseline ability to utilize the SPT as a stand-alone TCLE system. A dual task was added to evaluate individuals’ ability to complete a goal-oriented gait task and a secondary task under divided attention. Overviews of the design of these tasks is presented in Section 3.4. Results and discussion on participants’ ability to utilize the self-paced treadmill are the main topic of Chapter 4, and specific details of the SP/SPDT protocols can be found in Section 4.4. The HESA study incorporated two exoskeleton walking protocols, both repeated to allow for evaluation of how exoskeleton gait changes over time and as individuals accrued more experience with the system. Both protocols entailed walking for different lengths of time at various speeds on a fixed-pace treadmill to further assess any impact of walking speed on exoskeleton-augmented gait. Data from individuals’ initial exposure to the exoskeleton, Day 1 Initial Exo Walking, is presented in Chapter 5 of this thesis. Individuals were also asked to complete two cognitive surveys before and after the full protocol. HRI fluency surveys were implemented following operation of any TCLE system (i.e. the self-paced treadmill and the exoskeleton). All questions in these questionnaires are listed in Appendix C.

3.2 Participants

Fifteen healthy adults (8 male, 7 female) aged 18-29 years (mean ± SD, 23.6 ± 3.07 years) completed the HESA study. The experimental protocol was approved by the MIT Committee

48 Figure 3-1: Full two-day testing protocol

49 on the Use of Humans as Experimental Participants and all participants provided written, informed consent. Participants were excluded if they reported lower extremity injuries in the past six months which may have impeded protocol completion. Leg anthropometry was measured to enable data normalization to leg lengths.

3.3 Materials

3.3.1 Data Collection Systems

All data were collected in the high-end Computer Assisted Rehabilitation Environment (CAREN) System (Motekforce Link, Netherlands), located at the MIT Lincoln Laboratory Sensorimotor Technologies Realization in Immersive Virtual Environments (STRIVE) Cen- ter. The CAREN system is a 360o immersive virtual reality dome that enables collection of many different biomechanical measures. A marker-based optical motion capture system (VICON Industries, Inc., NY) was used to capture biomechanical data. Data collection for the SP/SPDT, and exoskeleton walking protocols utilized the lower-body Plug-In Gait biomechanical model with some marker adjustments for the exoskeleton. No motion data was captured for the modified Simon task. Individuals stood or walked on a treadmill atthe center of the CAREN system consisting of two independent treadmill belts. Individual force plates were situated under the separate treadmill belts (Motekforce Link) to collect ground reaction force data utilized in calculating reaction times in the modified Simon task (full protocol details can be found in Section 2.3.2). Throughout each experiment session, the D-Flow software (Motekforce Link) controlled the CAREN System and synchronized data acquisition.

3.3.2 The Dephy Ankle Exoskeleton

The goal of exoskeleton use is the ability to operate these systems in real-life contexts for gait augmentation. For exoskeletons to be effective in operational scenarios, they must assist or improve users’ walking ability. There may be any number of parameters by which effectiveness of an exoskeleton can be quantified. Currently, one commonly used metricisa reduction in metabolic cost during walking. There are a number of exoskeletons that have been found to reduce metabolic costs during walking in recent years [164], one of which is the Dephy ankle exoskeleton.

50 Figure 3-2: The Dephy ankle exoskeleton consists of a carbon fiber footplate integrated into a standard mid-ankle boot or, in the case of the men’s size 6, a low-ankle shoe (seen here). The boot-footplate assembly is connected to a shank assembly on which a unidirectional actuator is mounted.

The Dephy ankle exoskeleton (Figure 3-2) was chosen as the target system to investigate in this study. The Dephy system has shown decreases in metabolic cost during load carriage [122]. The system has the capability to build individual-specific control models based on inertial measures of an individual’s gait. It is also light-weight and self-contained (i.e. all components of the system can be worn on the user’s body), making the system relatively simple to implement and easily implementable in operational settings. These factors made the Dephy ankle exoskeleton a promising system candidate for testing in the HESA study.

3.4 Methods

What follows is a high-level overview of each cognitive test included in the HESA study and the Initial Exoskeleton Walking protocol. This thesis evaluates and discusses outcome measures from the modified Simon task, SP, and SPDT protocols, as well as Day 1 Initial Exoskeleton Walking. Detailed methods for those tasks are provided in the relevant chapters. Other perceptual, motor, and exoskeleton walking protocols are not outlined in this chapter but can be found in Appendix B.

51 3.4.1 Modified Simon Task

Participants put on 4-motor tactor system (details of the design found in Section 2.3.2) around their thighs such that two motors were positioned around each thigh, one medially and the other laterally. Participants stood on the platform in the CAREN system with one foot on each treadmill belt to enable collection of separate force measures from each foot. The CAREN system was sent vibratory cues to one or all the vibration motors in the tactor system. Participants were required to respond by tapping the appropriate foot according to which motor they felt vibrate. Motor cues placed on the lateral side of the thighs were mapped as congruent cues, e.g. a vibration on the right (lateral) side of the right thigh required a right-sided response, and motor cues on the medial side of the thighs mapped to incongruent responses, e.g. a vibration on the left (medial) side of the right thigh required a left-sided response (Figure 2-1C). Simultaneous vibration of all four motors indicated a no-go. Participants completed 100 total trials broken down into 20 congruent right-sided cues, 20 incongruent right-sided cues, 20 congruent left-sided cues, 20 incongruent left-sided cues, and 20 no-go cues. Order of cue presentation was randomized once, then that same cue order repeated for all participants.

3.4.2 Self-Pacing and Self-Paced Dual Tasking

Participants completed a speed achievement task on the treadmill within the CAREN system. In addition to the lower-body Plug-in Gait optical motion capture model, four markers were placed on participants’ hips, on four bony landmarks of the iliac - right and left anterior and posterior superior iliac. The midpoint of these four markers was measured as the individuals’ position on the treadmill. This position was utilized as the treadmill speed controller, which increased treadmill speed when the person was positioned nearer to the front on the treadmill, and slowed down with the person was positioned closer to the [178].

Participants were provided a general overview of the functionality of the treadmill speed controller and the speed achievement task. They had no prior knowledge of the specific target speed profile (Figure 3-3A). Visual feedback of the target speed and participants’ current speed (Figure 3-3B) was provided on the CAREN screen immediately in front of the treadmill throughout the entirety of the protocol. Participants were not given any time to practice with or acclimate to the self-paced treadmill, but began the SP protocol immediately. All

52 Figure 3-3: A) Schematic of the full target speed profile for the SP and SPDT protocols. B) Visual feedback of target speed and current measured speed provided to participants while they were competing the task (no speed values were provided to participants, they are provided here for reference). participants completed the SP protocol first followed by the SPDT protocol. The target speed profile for both the SP and SPDT protocols were identical. Visual and tactile go/no-go cues were provided during the SPDT protocol. Visual cues were presented as large red or green dots on the CAREN screen within the participants field of view, at each of the four corners of the visual speed feedback schematic. Tactile cues were presented via a vibration of a smartwatch worn on the participants left wrist, indicating the watch screen had turned either red or green. Participants were told to respond to all visual or tactile green cues by tapping the screen of the smart watch and not respond to red cues. A total of 200 cues were presented over the course of the SPDT protocol (specific details can be found in Section 4.4).

3.4.3 Initial Exoskeleton Walking

Participants completed a 19-minute exoskeleton walking protocol at the end of Day 1 of the HESA study. They donned the Dephy exoskeleton and immediately began the protocol with no training period with the exoskeleton. Participants were provided a general overview of the speed profile and timings of exoskeleton power changes (Figure 3-4) prior to beginning the task, but no timely warnings were given during the walking protocol. The Dephy system was powered off during the first four minutes of the walking protocol, where individuals’

53 Figure 3-4: Full speed and exoskeleton power profile for Initial Exoskeleton Walking (protocol was identical on both days). walked at four different speeds. The system built an internal control algorithm during these four minutes. Then walking speed was switched to 1.3 m/s for the duration of the protocol. The Dephy system was powered on for ten minutes of system adaptation, then powered off for five minutes of de-adaptation.

3.5 HESA Study in the Context of this Thesis

Here we presented an overarching study designed to explore many different sensorimotor and cognitive factors that may underlie individual variability in operation of TCLE systems, in this case specifically a powered ankle exoskeleton. A number of baseline tasks were implemented to measure aspects of participants’ sensory, cognitive, and motor abilities. Participants were then asked to walk with the Dephy ankle exoskeleton. The ultimate goal of this study was to assess individuals’ exoskeleton gait and adaptation over the course of two days and evaluate relationships between exoskeleton gait parameters with baseline functional measures quantified from a series of perceptual, motor, and cognitive tests. Protocol details only for the selection of cognitive tests that are the topics of this thesis were presented in this chapter. Protocol details for all other tasks in the HESA study that are not explored in this thesis can be found in Appendix B. Subsets of data from the SP/SPDT protocols are explored in Chapter 4 to investigate aspects task performance and gait strategy in the presence of a dual task and at different walking speeds. Chapter 5of this thesis delves into Day 1 Initial Exoskeleton Walking to assess individualized variation in exoskeleton gait characteristics upon initial exposure to an ankle exoskeleton system. Finally, Chapter 6 provides a preliminary investigation of what individual factors from the

54 modified Simon task and SP/SPDT protocols in the HESA study may underlie individuals’ exoskeleton gait characteristics.

55 56 Chapter 4

Gait Strategy & Dual Tasking with a Self-paced Treadmill

Now with a full HESA study protocol in place, we can address Aim 2 of this thesis: to characterize human gait strategy on a self-paced treadmill (SPT) system in the presence of a dual task. This will ultimately provide a deeper understanding of the interaction between task performance, attention, and gait with a TCLE system across individuals. Because the goal of this chapter is to provide an overarching picture of gait strategy and task performance with a SPT, we do not examine differences between individuals here. However, individualized measures of gait characteristics and task performance are utilized in Chapter 6 to assess what cognitive factors may underlie individualized variation in exoskeleton gait.

This chapter describes uses of SPTs within the wider context of TCLE systems. Back- ground is provided on the intersection of gait and attention, as well as gait particularly in the context of dual task paradigms, which interrogate attention during ambulation. This chapter then provides detailed methods of the self-pacing and self-paced dual tasking protocols in the HESA study. We contextualize the discussion with a characterization of individuals’ ability to complete the self-pacing task (i.e. achieve given target speeds on the SPT) without addition of a dual task. We consider how alternative secondary task input modalities impact secondary task performance and in turn are impacted by walking speed. We then examine the effects of varying walking speeds and presence of a dual task on gait strategy andtask performance and discuss implications for attention and stability with a SPT system.

57 4.1 Gait Modifications on Self-Paced Treadmills

Tightly-coupled lower-extremity (TCLE) systems (e.g. treadmills and hip/knee/ankle exoskeletons) give rise to new types of physical and cognitive human-robot interaction that can impact human gait strategy. For example, exoskeletons provided tactile and proprioceptive cues to the legs due to the tightly-coupled nature of the system that other devices do not. Resulting gait strategies can be observed via component gait characteristics (i.e. features of gait like stride lengths and widths, joint angle profiles, or muscle activation patterns). There is evidence that some aspects of gait change on a treadmill relative to over ground walking, e.g., metabolic cost [139], joint angles and cadence [3, 98], as well as ground reaction forces [197]. However, the literature is equivocal on how gait strategy changes on treadmills; some studies have found no operationally relevant differences in kinematic and kinetic parameters such as knee or ankle joint profiles and ground reaction forces [114, 157]. SPTs, a specific type of treadmill in which the person’s position on the treadmill is utilized asthe speed controller, have the potential to impact gait strategy given the novelty of the device and associated interactions. Gait on SPTs has been shown to yield shorter stride lengths, wider stride widths, and shorter stride times than on a fixed pace (FP) treadmill [178]. While these differences fell within natural stride-to-stride variability, they may still beoper- ationally relevant. Comparisons of gait on SPTs to overground are limited. One study found no significant differences in self-paced walking speeds on a SPT versus overground [144],but no gait characteristics were evaluated in this study. Another group compared SPT walking to overground in children with cerebral palsy, finding that these children walked slower, with shorter stride lengths and wider stride widths on the SPT [193]. However, it is unclear if these differences also exist in healthy adult populations. It is clear, however, that operation of a TCLE system have the potential to impact human gait characteristics in a variety of context-dependent ways.

4.2 Gait and Attention

When operating a TCLE in any sort of operational environment, additional activities must often be completed simultaneously, e.g. rehabilitation patients using an exoskeleton to restore or augment their gait while functioning in the community or while completing activities of daily living (ADLs) at home. Similarly, in treadmill use for rehabilitation, often the goal is

58 to enable individuals to return to normal functioning in the community, which requires ability to respond to environmental stimuli. Simultaneously completing multiple tasks requires the ability to selectively direct attention and work under varying levels of cognitive load. Multiple resource theory suggests that individuals have a limited set of cognitive resources to perform tasks [198] and performance decrements can be seen when cognitive processing resources for two tasks overlap [200, 199]. Accordingly, cognitive load can be inferred by task performance in dual task paradigms (i.e. decreased task performance indicates the secondary task adds cognitive load). A subset of these dual task studies incorporate gait as a primary task to evaluate attentional demands during walking.

Control of human gait likely entails both higher level cognitive processes like executive function, and lower level, more automated control. It is hypothesized that central pattern generators (CPGs) in the spinal cord give rise to rhythmic behaviors like locomotion [43, 67] with some independence from higher order control. This automation may decrease the directed attention required to maintain walking, allowing greater attentional resources to be utilized to complete secondary tasks. However, increasing workload on dual task performance during gait in elderly folks suggests walking is not fully automated and may require non-negligible levels of higher cognitive function and attention [203]. This cognitive requirement is also evidenced by lower coordination [145] and greater dual task losses during slower speed ambulation [146]. Understanding interacting effects of gait factors (e.g. speed) and cognitive factors (e.g. attention) on gait strategy and dual task performance can provide more context around interference between walking and secondary cognitive tasks.

4.3 Dual Tasking During Ambulation

Dual task studies that incorporate gait are often conducted on FP treadmills that impose artificial gait constraints and are therefore limited in exploring how gait factors like walking speed may impact gait or secondary task performance during a dual task. Performance during dual task studies is quantified both via observable gait characteristics and secondary task performance metrics (e.g. accuracy, reaction time). Gait characteristics during a dual task are impacted by walking modality (overground versus treadmill walking), e.g. stride time variability increased with a dual task overground but not on a treadmill [202], as well as secondary task type, e.g. gait characteristics changed in different ways for visuomotor

59 and serial subtraction tasks [127, 140]. Walking modality and secondary task type are both essential factors in operational use of TCLE systems. However, dual task paradigms on a FP treadmill limit an individual to a single speed, whether self-selected or not. This fixed speed constraint simplifies the walking task because no directed attention is required fromthe individual to maintain a specified speed. However, dual task studies performed overground often find a resulting decrease in walking speed [140, 13], possibly as a means of reducing workload or mitigating threat of balance or stability loss. The effect of treadmill walking speed on dual task performance is unclear. Schaefer [167] found that young adults’ secondary task performance on a working memory task was greater when walking at a (fixed) preferred walking speed relative to single-task performance, but not at non-preferred walking speeds. Others have found that errors on a secondary cognitive flexibility task increased with speed on a FP treadmill [191] agnostic of preferred walking speed. Allowing individuals to freely change speeds during a dual task paradigm could provide important insight into how walking speed and secondary tasks impact cognitive load.

Walking (either overground or on a treadmill) while completing a secondary task is extremely common in everyday scenarios. In particular, the widespread use of mobile phones, both while walking in the community (e.g. to check directions or respond to a text) or on a treadmill (e.g. to adjust music preferences during a workout), require individuals to maintain gait while responding to a tactile cue (in the case of a notification), often with directed visual attention and manual manipulation. Phone manipulation presents an operationally relevant secondary task with a variety of cue input modalities that may or may not require directed visual attention for an appropriate response. Studies have found that phone use while walking reduces gait speed [148] as well as gait characteristics like stride length and cadence [34]. These effects were similar in both laboratory and real-world environments [148]. Texting while walking also limits individuals’ ability to perceive and respond to visual cues in the environment [100], indicating that the tactile and visual attention required to text and walk limits attention from other activities. Thus, investigation of how presentation of tactile cues that require directed visual attention and manual responses may impact gait at varying walking speeds has the potential to provide extremely operationally relevant information.

In this study we investigated how walking on a self-paced treadmill while completing a secondary visual and tactile reaction time task impacted gait strategy and dual task performance. Specific target walking speeds were provided on the self-paced treadmill to

60 explore changes in gait strategy and dual task performance at varying walking speeds. We hypothesized that target walking speed and addition of a dual task would impact (i) target speed achievement, (ii) secondary task performance, and (iii) gait characteristics.

4.4 Materials and Methods

All participant information can be found in Section 3.2. This chapter focuses on the self- pacing and self-paced dual tasking protocols on day 1 of the full HESA study protocol. In-depth descriptions of the methods for those portions of the study follow.

4.4.1 Experimental Protocol

All data were collected in the high-end Computer-Assisted Rehabilitation Environment (CAREN) (Motekforce Link, Amsterdam, Netherlands). The treadmill in the CAREN system was configured to be self-paced. The system used each participant’s position onthe treadmill, measured as the midpoint of four optical motion capture markers on the waist (VICON Industries, Inc., Hauppauge, NY), as the treadmill speed controller (e.g. treadmill speed increased when the person was further forward on the system and decreased when the person was nearer the back). Further details on the self-paced treadmill control system are described in Sloot et al. [178]. Treadmill speed data was collected from the CAREN system at approximately 300 Hz. Reflective markers were placed on the body, configured using the lower-body Plug-In gait biomechanical model, for optical motion capture at 100 Hz. Participants also wore a TicWatch E smartwatch (Mobvoi Information Technology Company Limited, Beijing, China) on their left wrist for the dual task. The optical motion capture system, watch, and treadmill were connected via D-Flow software (Motekforce Link, Amsterdam, Netherlands) to enable time-synchronized data collection. Two tasks were completed as part of this study – achieving four different target speeds (0.5, 1.0, 1.3, and 1.5 m/s) on a self-paced treadmill with and without a simultaneous dual task (self-pacing without a dual task – SP; with dual task - SPDT). Participants were provided real-time visual feedback of their current speed relative to the target speed (Figure 4-1B), but they did not have prior knowledge of the full target speed profile (Figure 4-1A). No practice time with the treadmill was provided prior to beginning the protocol. When completing the SPDT, participants were not told to prioritize either the gait or secondary

61 task, rather they were asked to complete both as equally important. All participants completed SP first, then SPDT. The secondary task consisted of responding to intermittent visual and tactile cues. Visual cues were presented as red or green dots on the CAREN screen in front of the treadmill. Tactile cues were presented as watch vibrations indicating that the watch face had lit up either red or green. Participants were asked to respond to visual and tactile green cues by tapping the watch face and not respond to red cues. A total of 200 cues were presented over the course of the full trial: 77 tactile cues (35 red no-go cues) and 123 visual cues (47 red no-go cues). Cue presentation was pseudo-randomized throughout the protocol, with subsequent cues presented at intervals of 3-15 seconds. Cues were dispersed such that a minimum of 10 visual cues and 10 tactile cues were presented at all four target walking speeds and while participants were stopped (e.g. just prior to 100 seconds in Figure 4-1A). Participants were given the following scripts prior to completing each protocol to ensure consistent communication of task goals across participants:

SP instructions - “This treadmill takes your position on the system as the control input, so the further forward you are on the treadmill, the faster it goes, and it slows the further back you get. You will be presented target speeds on the screen in front of you. Your task is to reach that target speed and maintain it as best you can. The speed will change intermittently, move to that new speed as quickly as you can. The entire test should take about 15 minutes.”

SPDT instruction - “You will now compete the same self-pacing tasking you just finished, but this time you will be completing another task at thesame time. While walking, you will have to respond to spheres appearing at various points on the screen, or to the watch lighting up different colors. You will feel a watch vibration when it has changed color, so you should look at the watch when you feel a vibration. If the watch face is green, you should tap the watch face to respond. If the watch face is red, do not respond. Similarly, if the sphere appearing on the screen is green, you should respond by tapping the watch. If the sphere on screen is red, do not respond. Your goal is to respond as quickly as possible without making a mistake while also completing the self-pacing task accurately.”

62 Figure 4-1: (A) Target speed profile and representative data. Each speed target lasted for 75 seconds, considered one experimental phase (phases are numbered for reference). Four periods of 0 m/s were included, each lasting 12 seconds. Representative actual speed data from the SPT trial of one participant is shown. (B) Visual feedback schematic. The arrow pointed to participants’ actual speed, which moved up and down next to the ribbon. The green region was centered at the target speed and ranged 0.1 m/s below and above the speed, with the yellow regions also ranging 0.1 m/s above and below the green region. Participants were not given absolute speed values.

4.4.2 Data Analysis

Analyses were completed in MATLAB (Mathworks, Natick, MA). Data for each SP and SPDT trial were split into 10 experimental phases (Figure 4-1A). These phases began when the visual indicator on the screen first changed to a given target speed, and ended whenthe indicator changed again to a new target speed. Experimental phases were each 75 seconds long. Outcome measures (described below) were calculated for each experimental phase.

Seven measures related to speed achievement and gait characteristics were calculated - green time proportion (GTP), i.e. what proportion of the full phase measured speed fell within the green region (Figure 4-1B), speed ratio (ratio of mean measured speed to target speed), measured speed coefficient of variation (COV), normalized stride length (NSL), normalized stride width (NSW), stride time (ST), and normalized rise time (NRT). Measured treadmill speed data was used to quantify GTP, speed ratio, measured speed COV, and NRT. Rise time was calculated as time from the beginning of the experimental phase until measured speed reached the green region (0.1 m/s below or above the target speed, depending on whether the initial speed was less or greater than the new target speed). The speed change required in each phase was different (e.g. speed increased by 1.3 m/s in phase 1, butonly

63 0.5 m/s in phase 3). To enable comparison of NRT across different speed changes, rise times were normalized by the mean rise time across subjects for a given speed change. For example, mean rise time was calculated for all speed changes of 0.5 m/s (phases 2, 3, and 6) across subject and used to normalize rise time values for that speed change. Heel-strike and toe-off events taken from motion capture data were used to calculate NSL,NSW,and ST [134]. NSL calculations were modified to sum distance traveled by the treadmill during a given stride (dTM) and difference in antero-posterior position (x) between consecutive heel-strikes:

푥 + 푥 + 푑 푁푆퐿 = 1 n-1 TM n 퐿

Stride lengths and widths were normalized by participant leg lengths (L).

To determine if learning effects were present in how well individuals achieved target speeds when individuals first used a self-paced treadmill system, a repeated measures Anal- ysis of Variance (ANOVA) model was fit with dependent variable of GTP with factors of subject (random effect) and phase (10 levels) during SP. Post-hoc paired t-tests with aBon- ferroni correction for multiple comparisons were conducted when significant effect of phase was supported. Phase 1 during SP was found to be different from the other phases and therefore all Phase 1 data was excluded from subsequent analyses to remove learning effects.

To assess the effect of speed on secondary task performance during SPDT, accuracy of responses to visual and tactile cues was calculated. Two accuracy measures were calculated for each subject at each target walking speed and at standstill (target speed of 0 m/s) - percent accuracy for tactile cues, and percent accuracy for visual cues. Green and red cues were both incorporated into this calculation, with a correct trial defined as a response to a green cue or no response to a red cue, and vice versa for incorrect trials. There were a minimum of ten trials per subject for each speed and cue type. Due to data collection issues with the smartwatch and during the SPDT trial for some subjects, secondary task accuracy could be calculated for nine out of 15 subjects. A repeated measures ANOVA model was built for dependent measure of secondary task accuracy with factors of subject, trial type (visual or tactile) and speed (five levels – the four target walking speeds, and standing still, or target speed 0 m/s). Post-hoc paired t-tests with a Bonferroni correction for multiple comparisons were conducted when a significant interaction of type and speed was supported.

64 We next wanted to evaluate the effect of speed and presence of a secondary taskon gait task performance (i.e. ability to achieve target speeds as quantified by GTP, measured speed bias, and measured speed COV) and gait strategy characteristics (NRT, NSL, NSW, and ST). Due to the possibility of interrelations between these seven parameters, as well as possible relationships with walking speed, it was desired to include all parameters into a single multivariate, full factorial model. To accomplish this, all seven parameters were blocked together and incorporated as the dependent variable (DV) into one generalized linear mixed effect model (GLME) with predictors of Subject, Trial (1 - SP, 2 - SPDT),Speed (four levels), and a DVtype categorical predictor which coded for type of DV (seven levels, one for each DV). All four predictors were treated as categorical. Trial, Speed, and DVtype were fixed effects, while Subject was a random effect. A random interaction effectbetween Subject and Speed was also incorporated. Given that GTP was a proportionate variable and therefore not normally distributed, the GLME was built using a Poisson distribution. All DVs were rounded to the fourth decimal place and multiplied by 10000 so as to be treated as counts in the Poisson distribution. Predicted values for each DV across trial and speed were calculated based on fixed effects using the resulting model to assess impact of walking speed and presence of dual task on each DV while taking into account all related DVs. Dual task decrement (DTD) values for each DV per speed were calculated from the predicted values.

4.5 Results

4.5.1 Learning Effects

Participants’ ability to achieve target speeds (i.e. remain within the green region, as instructed) improved quickly following phase 1 and remained better than phase 1 from phase 2 on during SP (Figure 4-2). Repeated measures ANOVA model supported significant effect of phase on green time proportion, GTP (F(9,126) = 26.69, p < 0.001). Post-hoc paired t-tests indicated that green time proportion (GTP) increased and remained significantly greater than that of phase 1 for all phases (p < 0.001, corrected for multiple comparisons) with the exception of phase 7 (target speed 1.5 m/s), during which GTP was significantly greater than at phase 1, but less than all other phases.

65 Figure 4-2: Green time proportion increased immediately following phase 1 and remained consistent throughout all subsequent phases in SPT with the exception of phase 7. As- terisk (*) indicates significant difference from all other phases. Hash mark (#) indicates significantly greater than phase 1 and significantly lower than all other phases.

4.5.2 Effect of Speed on Dual Task Performance

Secondary task performance during SPDT was detrimentally impacted by walking speed more so when responding to tactile cues than visual cues (Figure 4-3). The ANOVA model supported a significant interaction effect of cue type and target speed (F(4,72) =7.78,p< 0.001). Post-hoc paired t-tests indicated that response accuracy to tactile cues was lower than that of visual cues (p < 0.004, corrected for multiple comparisons) for all target speeds except at standstill (target speed of 0 m/s) and a target speed of 1.5 m/s. Additionally, response accuracy to tactile cues was lower during target speeds of 0.5 and 1.0 m/s than at standstill and 1.5 m/s. Response accuracies at 1.3 and 1.5 m/s were not significantly different than standstill. Visually it can be seen that decreases in response accuracyare driven by lack of responses to green ‘go’ cues whereas response accuracy to red ‘no-go’ cues was nearly 100% across all speeds and cue types (Figure 4-4). Response accuracy to green visual cues was around 80% at all speeds, while accuracy to green tactile cues was 60% or lower.

66 Figure 4-3: Secondary task response accuracy by cue type and speed. Asterisk (*) indicates significant pairwise differences; hash mark (#) indicates significantly less than tactile accuracy at standstill and 1.5 m/s.

4.5.3 Effect of Speed and Dual Task on Speed Achievement and Gait Strategy

Walking speed and presence of a dual task significantly impact aspects of gait task performance and gait strategy characteristics in different ways. In some cases there was a nonlinear relationship with speed, and dual task decrement (DTD) often varied with increasing speed. The GLME indicated a strong fit 2(R = 0.95), with nearly all estimated coefficients found to be significant. Full GLME results are presented in Appendix A.1. Here we describe indetail results regarding effect of trial and dual task on each measure of gait task performance and gait characteristics. GTP remains within a range of 0.77 - 0.85 for all speeds with the exception of the 1.5 m/s, when there is a marked drop off in GTP both with and without a dual task (Figure 4-5a). Predicted values indicated greater decrements in GTP when dual tasking at slower speeds. DTD decreased (GTP becomes more similar across SP and SPDT) with increasing speed (Table 4.1), while predicted GTP at 1.5 m/s was higher with a dual task than without.

67 Figure 4-4: Secondary task response accuracy split by go/no-go (green versus red) cues. Average accuracy for green cues are stacked on top of average accuracy proportion for red cues at each walking speed for visual and tactile cues.

Speed ratio and COV showed similar responses to dual task and speed, both decreasing with increasing speed (Figures 4-5c and 4-5b). Predicted values showed a speed ratio closest to 1 (i.e. mean measured speed closest to the target speed) at 1.3 m/s. Slower speeds showed speed ratios above 1, indicating a bias towards walking faster than the target speed, while speed ratios at 1.5 m/s were less than 1, indicating a bias to walk slower than the target speed. DTD indicated that speed ratio in the presence of a dual task is expected to be further away from 1 at all speeds (Table 4.1). Predicted speed COV indicated greater precision (i.e. smaller COV) in speed achievement at faster speeds. Highest precision without a dual task was found at a speed of 1.3 m/s. Addition of a dual task led to lower predicted speed COV at 1.5 m/s.

NRT and NSW showed nonlinear changes due to speed. Longer NRT was predicted at the fastest (1.5 m/s) and slowest (0.5 m/s) speeds than at the two middle speeds (1.0 and

68 Table 4.1: Dual task decrements calculated from model predicted values for each gait performance metric and gait strategy characteristic. Negative value indicates a lower predicted value in the presence of a dual task.

Speed GTP Sp. Ratio Sp. COV NRT NSL NSW ST 0.5 -0.040 0.042 0.012 -0.204 0.023 -0.008 -0.012 1 -0.035 0.007 -0.003 0.029 0.016 -0.017 -0.020 1.3 -0.012 -0.004 -0.002 0.047 -0.012 -0.014 0.015 1.5 0.012 -0.002 -0.008 -0.188 0.020 -0.020 0.021

Table 4.2: Differences in predicted values between different target speeds for each gait performance metric and gait strategy characteristic. Negative value indicates lower predicted value at Speed 2 than at Speed 1.

Sp 1 Sp 2 GTP Sp Ratio Sp COV NRT NSL NSW ST 0.5 1 -0.001 -0.019 -0.047 -0.406 0.424 -0.007 -0.387 0.5 1.3 -0.015 -0.033 -0.058 -0.376 0.641 -0.013 -0.513 0.5 1.5 -0.084 -0.040 -0.057 -0.089 0.751 -0.008 -0.572 SP 1 1.3 -0.014 -0.014 -0.011 0.030 0.217 -0.006 -0.126 1 1.5 -0.083 -0.021 -0.010 0.317 0.327 -0.001 -0.185 1.3 1.5 -0.069 -0.007 0.001 0.287 0.109 0.005 -0.059 0.5 1 0.004 -0.054 -0.061 -0.173 0.418 -0.017 -0.395 0.5 1.3 0.013 -0.080 -0.072 -0.125 0.606 -0.019 -0.487 0.5 1.5 -0.032 -0.084 -0.076 -0.073 0.748 -0.021 -0.539 SPDT 1 1.3 0.008 -0.026 -0.011 0.048 0.189 -0.002 -0.092 1 1.5 -0.036 -0.030 -0.015 0.100 0.330 -0.004 -0.144 1.3 1.5 -0.044 -0.004 -0.004 0.051 0.141 -0.002 -0.052

1.3 m/s) (Figures 4-5d and 4-6a). DTD in NRT was also impacted differently depending on speed. NRT increased with a dual task at the two middle speeds while the slowest and fastest speeds showed faster NRT with the addition of a dual task (Table 4.1). In the absence of a dual task, narrowest NSW was predicted for a speed of 1.3 m/s, with widest NSW at the slowest speed. With the addition of a dual task, however, NSW decreased with increasing speed. Largest predicted DTD was seen at 1.5 m/s (Table 4.1) and smallest DTD at 0.5 m/s.

Speed impacted NSL and ST to a larger extent than did presence of a dual task. NSL was predicted to increase with speed, nearly doubling from 0.5 m/s to 1.5 m/s (Figure 4-6b) while ST was predicted to decrease from approximately 1.6 sec to 1.0 sec with the same speed change (Figure 4-6c). DTD of no more than 2% were found for predicted NSL and ST (Table 4.1). NSL generally increased in the presence of a dual task except at a speed of 1.3 m/s while ST decreased at the two slower speeds and increased at the two faster speeds.

69 Figure 4-5: Predicted values based on the GLME for green time proportion (GTP), Speed Ratio, Speed coefficient of variation (COV), and normalized rise time (NRT), the four metrics measured from treadmill speed during SP and SPDT. Error bars show standard error of predicted values incorporating impact of random effects, while the mean predicted value incorporates only fixed effects.

(a) (b)

70 Figure 4-6: Predicted values based on the GLME for normalized stride length (NSL), stride time (ST), and normalized stride width (NSW), the three gait characteristics during SP and SPDT. Error bars show standard error of predicted values incorporating impact of random effects, while the mean predicted value incorporates only fixed effects.

(a) (b)

(c)

71 4.6 Discussion

This study assessed how dual task performance and gait strategy were impacted by walking at different target speeds on a self-paced treadmill (SPT) system. The data support the hypotheses that target walking speed and presence of a dual task impact (i) individuals’ ability to achieve various target speeds, (ii) their secondary task performance on a visual and tactile reaction time task, and (iii) their gait characteristics. Here we discuss how amounts of attentional resources required for walking or completing a secondary task may differ at different walking speeds. In particular, differences in attentional resources required when near preferred walking speeds (PWS) relative to faster or slower speeds are discussed. Participants’ ability to learn to operate the SPT system are characterized. The importance of varying interaction modalities on dual task performance is highlighted. Finally the impact of speed and dual task on gait strategy characteristics is explored.

Walking at or near a preferred walking speed (PWS) requires fewer cognitive resources and may enable greater ability to complete complex dual tasks. PWS for young healthy adults is generally around 1.3 m/s [35, 109, 132], and participants in the present study were able to achieve target speeds with greater accuracy and precision at or near 1.3 m/s, both with and without a dual task. In the presence of a dual task, participants achieved best gait task performance (i.e. high GTP and a ratio of measured to target speed closest to one) at 1.3 m/s. A slowing of gait is sometimes observed in the presence of a dual task [49, 13, 48] when gait speed is not prescribed. We observed that, when asked to maintain a specific speed in the presence of a dual task, individuals did change their speed. However, rather than universally slowing, participants biased towards a preferred speed, i.e. they walked faster than the slowest target speed and slower than the fastest target speed. This preference towards a PWS indicates a lower attentional resource requirement when walking at speeds nearer PWS. Our findings contrast with other work that found dual task decrements witha backward counting task when walking at PWS and at 20% of PWS [128]. However, in that study participants completed dual tasks on a FP treadmill rather than a SPT. It may be that the added walking task complexity in the present study revealed a greater automaticity in PWS gait. Walking at slower speeds results in increased postural demands that are not present at faster speeds due to a larger proportion of the gait cycle being spent in double support. This increased postural requirement may result in increased cognitive demands

72 due to altered coordination patterns between limbs [145] or different patterns of muscle activation [40] in slow walking. The present study also observed greater cognitive demands at slower speeds. Walking at a PWS may require fewer cognitive resources to maintain the specified speed and thereby allow better dual task performance, i.e. faster RT. TCLEsystem design and operation should consider this interaction between speed and cognitive resource requirements, especially in contexts when walking at varying speeds may be necessary (i.e. crossing a street, maintaining pace in a crowd, etc). Take the example of an older adult walking with the aid of an exoskeleton across a busy street while using their phone. Their PWS may be slower than average due to their age, or perhaps they have a gait abnormality and use exoskeleton assistance. When using their phone, they may default to walking at their preferred slower speed, which could be dangerous in a crosswalk. In such a situation, it could be beneficial for the exoskeleton (or perhaps their phone) to provide a signal that they should wait to operate their phone until after crossing the street. Alternatively, the exoskeleton could increase the level of assistance provided to enable them to maintain a faster speed while utilizing the phone if phone operation is necessary in that moment. Broadly, when completion of secondary tasks becomes necessary in dual task contexts, it may be beneficial to provide additional system feedback to enable directed attention and support sustained gait and task performance.

Participants were able to quickly learn to achieve target speeds as instructed with the SPT system utilized in the present study, but continued to improve their system usage over time in other ways. Individuals spent significantly less time in the green speed region during the first phase of the SP protocol. After phase 1, however, time in green increased and remained high for the remainder of the SP protocol. While a number of studies have utilized self-paced treadmills as a means of investigating aspects of gait, such as differences in gait characteristics on a self-paced versus FP treadmill [178, 174] or overground [193], to our knowledge this is the first instance in which individuals have been asked to complete a specific speed-targeting task on a SPT system. Here we show that individuals are successfully able to complete this task after a short period (75 seconds) of learning. However, predicted values of NRT in the present study provide evidence of a slower learning process with the SPT. One would expect that the added workload from a dual task would increase the time required to achieve a target speed. This was observed only at target speeds of 1.0 and 1.3 m/s. At the lowest and highest speeds, however, NRT was much faster in the presence of

73 a dual task. It is possible participants integrated sensory feedback from the SPT controller (e.g. response lag, magnitude of response with varying human motion) over the course of the SP protocol, resulting in the ability to more quickly achieve low and high target speeds during SPDT. Distinct fast and slow learning processes as observed by different measures are consistent with motor adaptation literature. For example, split-belt intra-limb adaptation as measured by kinetic and kinematic parameters occurs quickly while interlimb coordination takes more time to achieve [153, 115]. Features of TCLE systems can impact how quickly individuals adapt to the system, e.g. greater assistance torque with an exoskeleton yielded longer timelines for muscle activation patterns to reach a steady state [90]. Presently, nuances of the SPT controller (i.e. sensitivity) may have led participants to initially select a cautious strategy in reaching the target speed. With more experience, they felt comfortable more quickly targeting non-preferred walking speeds. NRT was the only parameter in which this slower learning was observed, possibly because NRT was the only parameter that probed a short term change during speed transitions. All other measured parameters assessed steady states (i.e. means across a full 75-second experimental phase), indicating that steady state operation has a faster learning curve, but the learning curve for transition states is slower. While individuals may quickly learn to operate TCLE systems as instructed, it is important to assess longitudinal changes in other measures that may illustrate slower learning or adaptation processes.

Cue input modality of secondary tasks can lead to differential task outcomes based on walking speed. In the present study, accuracy on a dual task was detrimentally impacted by walking, but only at certain speeds and differently for tactile and visual cues. Response accuracy to tactile cues was lower than that to visual cues at all walking speeds except at 0 (standstill) and 1.5 m/s (although a lack of significant effect at 1.5 m/s may have been due to a small sample size). Tactile cues can be less robust in some cases than visual cues. For example, movement can gate (i.e. restrict) sensory signals [44], which can lead to losses in perception during walking. In the present study, sensory signals from the wrist could have been gated due to arm swinging motions while walking, which would result in lower accuracy in green ‘go’ cues than to red ‘no-go’ cues, as observed. Decreased task performance with tactile cues is also consistent with our previous findings in the modified Simon task (Section 2.5). However, previous work has shown response accuracy of over 90% to wrist-worn tactile cues in the presence of a secondary visual scanning task [97], though it should be noted

74 that there was no movement or walking required in that study. Even still, the nature of the tactile cue itself is not the only reason for worse task performance observed in the present study. Rather, decremented accuracy to tactile and visual cues during walking varied by walking speed. While some work has found no effect of two different secondary task types (i.e. a motor task requiring transfer of items between pockets and a serial subtraction task) on gait dual task performance [170], most dual task studies that incorporate walking as one of the tasks entail unconstrained walking, i.e. no specific gait-related task goals. Individuals often slow overground walking in the presence of secondary tasks, e.g. with visual RT and Stroop tasks [140] as well as a serial subtraction task [13]. When walking on a FP treadmill, no attention need be directed toward walking speed, though gait characteristics may change to maintain the fixed speed [174]. However, when the walking task requires active speed monitoring, as with the present study, greater attentional demands may lead to dual task decrements in certain cue types. For example, when individuals were asked to place their foot within a particular target location while walking, Sparrow et al. [180] observed decrements to a visual secondary task but not an auditory secondary task, likely due to interference between needing to simultaneously visualize the target foot location and the visual cue. The current study presented a constrained walking task that required directed attention to a speed feedback indicator. Visual cues were presented near that indicator whereas tactile cues were presented on the wrist, requiring a shift in visual attention. This necessary shift may have led to decremented response accuracy to green ‘go’ tactile cues. It may also be that walking at slower speeds directed attention away from individuals’ ability to coordinate their arm motions to properly respond to the cue (i.e. tap the smart watch face with the other hand). Tactile cues arise naturally in TCLE systems given the tight physical interaction with the user. However, responses to cues that are presented using a tactile interface on TCLE systems should not require a shift in visual attention or coordination of complex motions to minimize interference with walking in complex operational settings.

Walking speed and presence of a dual task impact distinct aspects of gait stability. Wider NSW indicates greater mediolateral (ML) stability [116, 160]. Presently, the addition of a dual task led to decreased ML stability as observed by decreases in individuals’ NSW across all speeds. These decreases varied by speed. Walking at 0.5 m/s showed the smallest dual task decrement (DTD) in NSW while the other three speeds showed decreases of 5-10% indicating that, when walking near PWS, individuals may allow greater attentional resources

75 towards a secondary task at the expense of some gait stability. In contrast, anteroposterior (AP) stability as measured by NSL and ST seems more dependent on speed than presence of a dual task. It is expected that NSL would increase and ST would decrease with increasing speed [66], as was presently observed. Changes to NSL and ST due to a dual task were around 2% for all speeds (NSL increased with dual task except at 1.3 m/s, ST decreased with dual task at the two slowest speeds and increased at the two faster speeds), while changes in speed led to near doubling or halving of NSL and ST, respectively. Changes in NSW due to dual task and speed, on the other hand, were of the same magnitude. Further, present changes in SL and ST fell within normal walking variability in the presence of a dual task as observed in other studies [13, 48]. Thus, it seems walking with a dual task impacts ML stability while AP stability may be more dependent on walking speed. This knowledge can inform the use of treadmills as rehabilitation and gait training systems. For example, a patient post-stroke attempting to rehabilitate their gait on a treadmill to improve independent functionality may need to learn to shift their ML and AP stability differently for different activities of daily living. Training them at different speeds may helpthem modulate AP stability, while training them in the presence of a dual task could assist in ML stability modulation. These adaptations could help them as they transition to community ambulation and lessen their risk of falling.

Dual task literature provides a wide variety of perspectives on how factors such as walking speed and task complexity impact dual task performance and gait characteristics. Some believe that minimal attention is required to control rhythmic walking during a dual task [13]. However our work indicates that the amount of attention required for gait control is dependent upon gait speed. Greater levels of attention are required to walk at slower speeds and individuals bias towards a preferred walking speed when attention becomes a limited resource during a dual task scenario. The ability to maintain gait speed can be an important feature of activities of daily living. For example, researchers found that older adults, when asked to walk faster than their PWS while completing a dual task, could not walk at a fast enough speed (approx. 1.2 m/s) to safely cross a crosswalk [47, 51]. Patients with gait abnormalities may also walk with slower speeds that could interfere with community ambulation. While slowed gait speed at one’s baseline may be an issue for a particular set of individuals, i.e. older adults or people with gait abnormalities, changes in walking speed in everyday scenarios are relevant even to young adults. Distractions abound while walking

76 around our communities, and young adults have also been shown to change gait speed and gait characteristics with the addition of dual tasks [140, 13]. TCLE systems like exoskeletons can help increase walking speed or otherwise modulate gait characteristics, but care must to taken to ensure user safety in such scenarios. Targeted feedback during TCLE system use to direct attention towards a gait or secondary task goal can be used to support user safety and coordination with the system. The method by which such feedback is presented should be thoughtfully considered. While a number of studies have shown benefits of using multimodal cue presentation to enhance secondary task performance [80, 96], there is limited information on comparisons across cue input modalities and any associated impact on task performance of both gait and secondary tasks. TCLE systems present tactile cues by nature of the physical interaction between the user and the system, making tactile cue presentation a convenient feedback mode. However, we found that tactile cues that require visual and manual responses may interfere with gait tasks. Therefore, the design of such tactile cues must be presented in a way that minimizes interference, for example by supporting responses that do not require directed visual attention. Such cue design is already utilized in consumer products, e.g. the implementation of voice control for mobile phone notifications to enable responses with minimal need for directed visual attention or physical manipulation of the device.

4.7 Conclusion

The goal of this study was to understand how walking speed and simultaneous completion of secondary tasks impact task performance and gait strategy. We characterized individuals’ ability to achieve target speeds on a self-paced treadmill with high accuracy and precision within 75 seconds of first being exposed to the system. In the presence of a dualtask implemented using tactile cues that required directed visual attention and a manual response (i.e. cue and response interaction modalities similar to a mobile phone), we show that walking at or near preferred walking speeds enables greater dual task performance. Walking speed also impacts individuals’ task response accuracy. In particular, slower walking speeds lead to task interference with secondary tactile cues. Gait strategy characteristics are also impacted by walking speed and the presence of a dual task, with secondary tasks leading to less ML stability in individual’s gait. Dual task performance can be extremely sensitive to

77 the nature of the motor and secondary tasks involved [203]. Novel or unfamiliar motor tasks may lead to greater DTD, indicating a greater attentional resource requirement, whereas completing a secondary task without a specified walking speed or other gait task may allow for greater cognitive resources to be directed towards the secondary task. TCLE systems provide a constraint on walking due to the tight physical coupling with the operator. This physical constraint may force the use of greater attentional resources when walking with TCLE systems, which has implications for how well individuals might perform secondary tasks while operating such systems. To enable the operational use of TCLE systems, e.g. for patients with gait impairments to ambulate independently in their communities or for healthy factory workers to utilize an exoskeleton to increase productivity or decrease injury risk, it is essential that TCLE systems be designed such that cues from the system itself do not detract attention from any secondary tasks the operator must complete or limit their ability to achieve specified walking tasks. We have now characterized and examined the impact of walking speed and dual task presence on gait characteristics and secondary task performance across individuals, showing that individuals learn to operate a SPT quickly and continue to refine their use of the system over time. Different walking speeds impact individuals’ gait strategies onthe SPT, in particular AP stability as measured by NSL. Addition of a dual task also impacts gait characteristics, in this case ML stability as observed via NSW. This chapter assessed population-level effects of walking speed and addition of a dual task to understand aspects of gait strategy and attention in the context of one particular TCLE system, a self-paced treadmill. Individualized data from this chapter will be revisited in Chapter 6 and used as candidate predictors for individualized exoskeleton gait strategies.

78 Chapter 5

Individualized Exoskeleton Gait Strategies

This thesis has explored a number of factors necessary for individuals’ to successfully operate tightly-coupled lower-extremity systems. Chapter 2 of this thesis explored alternative interaction modalities relevant to TCLE systems, which enable tactile cue presentation by nature of being tightly physically coupled to the body and require responses using the lower- extremity. We described a comprehensive protocol designed to investigate sensorimotor and cognitive factors underlying individual variation in human gait with an exoskeleton (Chapter 3). We presented data from a subset of the HESA Study, exploring, in depth, individuals’ ability to operate a new TCLE system, the self-paced treadmill (SPT). Chapter 4 further explored the impact of walking speed and presence of a dual task on attention and gait strategy with the SPT. We now come to the exoskeleton protocol on Day 1 of the HESA study and investigate how individuals’ gait characteristics change with an ankle exoskeleton to address Aim 3 of the thesis. This chapter provides background on individual gait variability during exoskeleton use and associated impacts on stability, then discusses in detail the gait characteristics measured to quantify aspects of individuals’ gait while walking with a powered ankle exoskeleton. The chapter concludes with a discussion on the similarities and differences found across individuals’ gait characteristics when initially operating apowered ankle exoskeleton and implications for user stability and safety.

79 5.1 Probing Gait Strategy via Observable Gait Characteris- tics

Lower-extremity exoskeletons are wearable robotic systems which augment individuals’ gait. When first donned, individuals are typically given time to explore moving with the system. During exploration, gait characteristics (features like metabolic cost, muscle activation patterns, and stride lengths or widths) can be observed. Differences in these gait characteristics would suggest individuals utilize varying gait strategies during initial exoskeleton operation. Here we define strategy as any method used to achieve a task or goal, e.g., the unique stepping and muscle coordination patterns as one walks while maintaining balance. Design- ers may assume that consistent strategies are used for a given exoskeleton controller. For example, if assistive power is added to the ankle when plantarflexing, one might assume that operators will increase stride lengths upon system activation. However, it is unclear if operators initially adhere to these expectations. Individuals may initially react to exoskeletons differently based on factors like physical fitness, comfort with novel devices, orprior experience with that or similar systems. Exoskeletons are designed to assist individuals; thus, it is important to understand individualized variation in human strategy when using an exoskeleton to inform training and system design.

5.1.1 Individual Variation in Exoskeleton Gait

It is common in human studies to present average changes in dependent variables across subjects in response to independent factors, e.g. gait characteristics in response to exoskeleton power. However, the average perspective of how people interact with exoskeletons may obfuscate unique phenotypic responses of individual operators. Exoskeleton literature supports variation in how individuals adapt, or change their walking strategy over time, with powered exoskeletons, e.g. via decreasing metabolic cost [122, 61, 165] or changes in surface electromyography (EMG) and walking kinematics [65]. Gordan et al. [65] observed that when participants are pooled, walking kinematics change suddenly upon addition of exoskeleton power and the difference from baseline decreases during 30 minutes of adaptation. Surface EMG showed a different pattern, remaining near baseline levels upon initial exoskeleton activation, then decreasing during 30 minutes of powered walking [65, 90]. These studies present a generalization using averages across individuals with standard deviations

80 based on the individual participants. Anecdotally, many researchers comment that some people are better at using exoskeletons. For example, Sawicki [165] observed reductions in metabolic cost over three separate experimental sessions, with a subset of participants showing increased costs during the first session while others showed a decreased metabolic cost. Kao et al. [90] observe that half their participants reached steady-state muscle activation patterns after two 30-minute exoskeleton walking sessions, while the other half did not. One explanation may be that these individuals selected different strategies when using the exoskeleton, leading to the observed variation in performance. Humans adopt gait strategies that optimize for gait goals (e.g., stability, metabolic efficiency) based on current task requirements and given instructions. Historically, lower- extremity exoskeletons have been designed and tested during steady state walking and have been shown to reduce metabolic cost in some subjects [165, 32, 4]. However, this task may not show the benefits of exoskeletons in gait tasks where humans optimize for other goals. For example, when walking downhill, people chose a less energetically optimal gait pattern that showed decreased stride period variability [81], an indication of greater stability [72]. The walking goals can be implicit or explicitly provided, which may impact gait characteristics. When told to walk in a relaxed manner downhill and allow gravity to assist, metabolic cost was lower than when participants were instructed to walk in a careful manner with their legs beneath them, or when their walking was threatened by external perturbations [121].

5.1.2 Stability

Gait characteristics like normalized stride length (NSL) and width (NSW) are component measures of higher order constructs like human gait goals and strategy (e.g. stability). For example, Monsch [121] observed changes in stride length and width in addition to metabolic cost during downhill walking. Decreasing NSL increases anteroposterior stability [69]. In- creasing stride width produces greater mediolateral stability by increasing the base of support. Individuals have been found to walk with shorter NSL and wider NSW to prioritize stability, such as when presented with surface and visual field perturbations during walking [116]. Anticipation of slips or falls led individuals to decrease NSL [27] while externally provided lateral stabilization allowed individuals to walk with narrower NSW [38]. Exoskeletons are systems that provide external perturbation during walking. Studying metrics like NSL

81 and NSW in the context of exoskeleton use can provide insight into individuals’ stabilization strategies. The goal of this study was to describe individual strategies utilized during initial powered ankle exoskeleton walking and how individual strategies change over an initial exposure period. We hypothesized that there will be variation in how individuals alter their gait strategies in response to (i) exoskeleton power state (OFF-baseline, ON, OFF-deadapt), (ii) the duration of time spent in an active state (adaptation), (iii) the duration of time spent in an off state following active (de-adaptation), and (iv) walking speed.

5.2 Materials and Methods

All participant information can be found in Section 3.2. This chapter focuses on the exoskeleton walking protocol on day 1 of the full HESA study protocol. An in-depth description of the methods for that portion of the study follows.

5.2.1 Experimental Protocol

All data were collected in a high-end Computer-Assisted Rehabilitation Environment (Motek- force Link, Netherlands). Reflective markers consisting of the lower-extremity Plug-in Gait model with adjustments for the exoskeleton were placed on the body and exoskeleton for optical motion capture at 100 Hz (VICON Industries, Inc., NY). The sensors, treadmill, and exoskeleton were connected via D-Flow software to enable time-synchronized data collection. Participants completed testing while wearing the Dephy powered ankle exoskeleton (Dephy, Inc., Maynard, MA) [122, 75]. The exoskeleton provided torque at push-off during stance phase of the gait cycle and became transparent to the user at all other points in the gait cycle after creating an individualized model. All participants used one of four available boots sizes (men’s 6, 8, 10, and 12); participants with in-between foot sizes chose which boot felt more comfortable. One trial lasted 19 minutes and participants wore the exoskeleton throughout. The first four minutes consisted of four speeds (0.5 m/s, 1.0 m/s, 1.3 m/s, and 1.5m/s) for one minute each with the exoskeleton unpowered (OFF-baseline period). The exoskeleton created a participant-specific control model during these four minutes. The exoskeleton was activated three seconds after treadmill speed dropped from 1.5 m/s to 1.3 m/s. Participants

82 Figure 5-1: Exoskeleton walking protocol overview and representative normalized stride length (NSL) data. walked at 1.3 m/s with the exoskeleton activated for 10 minutes (ON period), then the exoskeleton deactivated and participants walked for five minutes (OFF-deadapt period). All data was binned into nine walking conditions. Four walking conditions were defined during OFF-baseline, consisting of 30 seconds in the middle of the one-minute walking period at a given speed. Three adaptation walking conditions were defined during the ON period, each two minutes long: early phase activated walking (A1), mid phase activated walking (A2), and late phase activated walking (A3). A1 begins 15 seconds after exoskeleton power-on, A3 ends 15 seconds before exoskeleton power-off, and A2 is in the center of the 10 minutes. Two de-adaptation walking conditions were defined during the OFF-deadapt period, also two minutes long: early phase deactivated walking (D1), starting 15 seconds after exoskeleton power-off, and late phase deactivated walking (D2), ending 15 seconds before the end of the protocol. Participants were not provided specific instructions regarding gait strategy when walking with the exoskeleton. Participants were provided an overview of the exoskeleton walking protocol including information regarding timing of speed changes and exoskeleton power on and off prior to beginning the trial, but no timely warnings were given during the exoskeleton walking protocol.

5.2.2 Data Analysis

All data analyses were completed in MATLAB (Mathworks, Natick, MA). Heel strike and toe-off events were detected from motion capture data [134]. Stride lengths andwidths

83 were calculated from these gait events and normalized by participant leg lengths. Distance travelled based on treadmill speed was incorporated into stride length calculations. Gait characteristics were binned into nine walking conditions (Figure 5-1). To assess hypotheses 1-3, effect of exoskeleton power state, time spent in active state, and time spent in de-active state, separate repeated measures analysis of variance (ANOVA) models were fit for NSL and NSW with factors of participant (random effect) and walking condition (six levels: B3, A1-3, D1-2). Here we considered only the conditions when walking at 1.3 m/s. To assess hypothesis 4, the effect of speed on strategy, separate repeated measures ANOVA models were fit for NSL and NSW with factors of participant (random effect) and speed (four levels). Only baseline walking conditions (B1, B2, B3, and B4) were considered in this analysis to exclude exoskeleton power state as a variable. Significant interactions with the participant factor were found in all models, so followup participant-specific one-way ANOVA models were constructed with Tukey post-hoc tests. Similarities and differences of these pairwise comparisons were considered to understand participant-driven variation in strategy across walking conditions. To assess relationships between changes in NSL and NSW, correlation coefficients were calculated for subject-specific difference in means for NSL versus NSW for selected walking condition pairs (i.e. B3-A1, A3-A1, A3-B3, D1-A3, D2-D1, and D2-B3).

5.3 Results

5.3.1 Effect of Exoskeleton Power State

Individuals’ walking strategies as measured by NSL and NSW differed across exoskeleton power states and varied over time. ANOVA models for NSL and NSW indicated a significant interaction effect of the participant and walking condition factors (NSL ANOVA results- F(70,8707) = 39.71, p < 0.001, 휂2 = 0.0544; NSW ANOVA results - F(70,8707) = 12.09, p < 0.001, 휂2 = 0.0263). These interaction effects of participant and walking condition support that participants selected different gait characteristics, which were examined with post-hoc tests. Box plots of NSL and NSW for representative participants are presented in Figure 5-2. Individuals changed NSL in different ways across walking conditions (effect sizes forall comparisons in Figure 5-3) on page 87). Different gait characteristics were observed across

84 Figure 5-2: Representative boxplots of gait characteristics (A. Normalized stride length; B. Normalized stride width) binned by walking condition. Only walking conditions at a speed of 1.3 m/s are shown. Participants were selected to highlight the variety of behaviors observed. Some participants showed no significant changes across any walking condition in NSL or NSW, such as EXO105 and EXO104, respectively. Some showed increases in NSL or NSW from baseline (B3) to early activated walking (A1), such as EXO111. Others showed decreased NSL during that time (e.g. EXO115). EXO111 showed gradual increases in NSL during adaptation (A1-A3) while EXO115 showed a larger increase from A1 to A2, followed by a decrease from A2 to A3. Participants also showed different variances in NSL and NSW (e.g. EXO104 shows larger variance in NSW than EXO107). Significant differences are not shown. Boxplots for all participants can be found in Appendix A.2.

85 participants due to changes in exoskeleton power state, i.e., from unpowered to powered (A1-B3) and vice versa (D1-A3), as seen by inconsistent changes (i.e., significant increases, decreases, and no significant changes) in NSL. Conversely, consistency in strategy across most participants was observed when examining time within an exoskeleton power state. Twelve of 15 participants showed significantly increased NSL during adaptation (A3-A1) and 11 of 15 participants showed no change during de-adaptation (D2-D1). Participants showed different strategies after 10 minutes of adaptation as observed by inconsistent changesin NSL during late phase adaptation relative to baseline (A3-B3). After five minutes of de- adaptation, nine of 15 participants maintained increased NSL (D2-B3) with the other six participants returning to baseline NSL. Differences in strategy were also observed with NSW (effect sizes for all comparisons in Figure 5-4 on page 88). Eight of 15 participants showed no significant change in NSW in response to exoskeleton power turning on and off (A1-B3 and D1-A3, respectively). Seven participants showed increased NSW in response to addition of power while a different set of seven showed decreased NSW in response to power removal. Conversely, consistency was observed in walking strategies during adaptation (A3-A1), with 12 of 15 participants showing decreased NSW. A different set of 12 showed no significant change during de-adaptation (D2- D1). Individuals showed inconsistent changes in NSW relative to baseline after 10 minutes of adaptation (A3-B3). After five minutes of de-adaptation (D2-B3), eight of 15 maintained a decreased NSW with the others showing no significant change. There was a moderate relationship between how individuals change their NSL and NSW after five minutes of de-adaptation relative to baseline, D2-B3 (r = -0.633,R2 = 0.401, 95% CI [-0.865, -0.179] ). No strong relationships were found in any other comparisons across 2 2 2 2 walking condition pairs (R -values were RA1-B3 = 0.035; RA3-B3 = 0.017; RA3-A1 = 0.028; 2 2 2 RD1-A3 = 0.079; RD2-D1 = 0.173; RD2-B3 = 0.4011).

5.3.2 Effect of Walking Speed

Participants increased NSL with increasing speed by different amounts. Repeated measures ANOVA model for NSL during baseline walking conditions supported significant inter- participant variation (F(14,1529) = 16.97, p < 0.001, 휂2 = .0605) and effect of speed (F(3,1529) = 1116.69, p < 0.001, 휂2 = 0.8667). A significant two-way interaction of participant-walking condition (F(42,1529) = 8.2, p < 0.001, 휂2 = 0.0109) resulted in follow-

86 Figure 5-3: Pairwise comparisons of difference in normalized stride length (NSL) across selected walking conditions. A) Individual participant differences for each pair of walking conditions. Significant differences (p < 0.05) are marked by an asterisk. B) Valuesof Cohen’s d and effect size for each participant across pairwise comparisons when a significant difference was found. The positive or minus sign next to effect size denotes apositiveor negative difference (i.e. positive means the first written condition of the pair hadgreater NSL, negative means the first condition showed a lower NSL). Colored boxes to the leftof participant numbers in B) indicate matched participant color bars in A).

87 Figure 5-4: Pairwise comparisons of difference in normalized stride width (NSW) across selected walking conditions. A) Individual participant differences for each pair of walking conditions. Significant differences (p < 0.05) are marked by an asterisk. B) Valuesof Cohen’s d and associated effect size for each participant across pairwise comparisons when a significant difference was found. The positive or minus sign next to effect size denotesa positive or negative difference (i.e. positive means the first written condition of the pairhad greater NSW, negative means the first condition showed a lower NSW). Colored boxes to the left of participant numbers in B) indicate matched participant color bars in A).

88 on within-participant ANOVA models and post-hoc comparisons supported that NSL increased with increasing speed and the interaction effect resulted from differences in magnitude of change in NSL (Figure 6A). Participants showed inconsistent changes in NSW due to different speeds. Repeated measures ANOVA model for NSW during baseline walking conditions supported significant inter-participant variation (F(14,1529) = 71.64, p < 0.001, 휂2 = 0.7308) and effect of speed (F(3,1529) = 11.27, p < 0.001, 휂2 = 0.0250). Significant two-way interaction of participant-walking condition (F(42,1529) = 6.33, p < 0.001, 휂2 = 0.0311), as well as follow-on within-participant ANOVA models and post-hoc comparisons supported different emergent strategies. Four participants (103, 104, 105, 112) showed no significant differences in NSW across speed. The other 11 participants varied in their behaviors, eight of which showed significantly greater NSW during the slowest speed (Figure 6B).

5.4 Discussion

In this study, gait strategies were evaluated during initial walking with a powered ankle exoskeleton. The data support the hypotheses that individuals select different gait strategies as quantified through normalized stride length and width. Here we considered strategy in the context of anteroposterior (AP) and mediolateral (ML) stability inferred from NSL and NSW. We discuss similar trends observed overtime within exoskeleton power states (i.e. adaptation and de-adaptation) and inconsistent changes in gait characteristics due to changes in exoskeleton power state (i.e. off to on, and on to off). We then show that individuals seem to change NSL and NSW independently during early exoskeleton operation without explicit use instructions. Consistent trends were observed in how individuals’ strategies change overtime during adaptation, but individuals did not converge to the same strategies. Most individuals showed increased NSL and decreased NSW over ten minutes of adaptation (A3-A1), suggesting a similar strategy as walking at faster speeds. To contextualize adaptation with effect of walking speed, we observe that an increase in NSL of approximately 0.1 during powered exoskeleton adaptation at 1.3 m/s is similar to the increase in NSL during a speed increase from 1.3 to 1.5 m/s with an unpowered exoskeleton. For NSW, narrowing was observed during both powered exoskeleton adaptation and with faster walking speeds, consistent with

89 Figure 5-5: A) Normalized stride length (NSL) shown as averages across all participants for OFF-baseline conditions at four speeds (B1 – 0.5m/s, B2 – 1.0m/s, B3 – 1.3m/s, B4- 1.5m/s). Participants significantly increased NSL with speed to different magnitudes across both trials. Average participant NSL was significantly different for all speeds. B) Normalized stride width (NSW) boxplots for baseline speed bins for three representative participants. EXO104 showed no difference in NSW across speeds, while EXO113 showed widest NSWfor the slowest speed. EXO117 showed wider NSW at B1 and B2 (not significantly different from each other) than B3 and B4 (not significantly different from each other). Significant results from participant-specific ANOVA models are denoted. NSW boxplots for all participants can be found in Appendix A.2.

90 the literature [135]. However, not all participants chose longer NSL or narrower NSW after 10 minutes of adaptation relative to their baseline (A3-B3), nor did participants converge to the same NSL or NSW. Powered exoskeleton walking seems to encourage consistent changes in gait similar to strategies used in faster walking over time, indicating that exoskeletons can be used to encourage specific gait characteristics with some experience. However, specific instructions may be required to encourage consistent strategies across people during transitional modes, such as exoskeleton power state changes.

Five minutes of de-adaptation may not provide enough time to return to baseline strategy metrics, as most participants showed no change in components of gait strategy with five minutes of de-adaptation (D2-D1). Additionally, NSL and NSW were different than baseline for some participants even after five minutes of de-adaptation (D2-B3), indicating that five minutes may not be enough time for complete washout. Instead, some individuals maintained their powered exoskeleton walking strategy. It is unclear how long the new strategy would be maintained and should be further investigated. It may be that exoskeletons can be used to encourage specific strategies with only intermittent operation.

While exoskeleton operation was intuitive in that all users could walk with the system immediately, individuals explored NSL differently in the absence of prescribed guidelines. One might expect increased NSL upon addition of exoskeleton power during push off to account for energy added to the system. This response was not observed across all users. The data support inconsistent changes in NSL when the system was first powered on (A1- B3). Decreases in NSL may signify users that were not immediately comfortable with the additional power from the system. They may have forced their foot to the ground sooner than metabolically optimal, a strategy observed in other studies to enable increased stability in response to perturbations [121, 116, 27], which yields a larger anteroposterior margin of support [68]. Even after 10 minutes of adaptation, three individuals continued to select shorter strides relative to baseline (A3-B3), indicating continued use of a strategy that may support greater anteroposterior stability. It is also possible people did not immediately understand how the system might benefit them and selected strategies contrary to metabolic efficiency. While metabolic cost was not presented here, shorter step lengths would require increased muscle activation to place the foot on the ground in opposition to forward pro- gression of the leg due to added power. Increased NSL may align with participants not “fighting” added exoskeleton power to place the foot on the ground, thus expending less

91 energy. The current results support encouraging longer step lengths during initial training to enable metabolically efficient adaptation.

Exoskeleton operation impacts individuals’ ML stability differently as measured by NSW. We might expect that individuals increase NSW upon addition of exoskeleton power to widen their base of support, as observed in other perturbation studies [116, 160]. Presently, only half of participants showed this behavior. Eight participants showed no significant change with addition of exoskeleton power (A1-B3) and a different set of eight showed no significant change with removal of power (D1-A3), indicating that some individuals do not change their ML stability due to exoskeleton power changes. Inconsistent changes in NSW were observed after 10 minutes of adaptation relative to baseline (A3-B3), meaning participants chose disparate stabilization strategies. Some participants may have decreased NSW because 10 minutes of exoskeleton adaptation provided a stabilizing effect, as externally provided stabilization has been shown to yield narrower stance widths [38]. Conversely, participants with increased NSW may have tried to enable ML stability by consciously or subconsciously widening their stance. Voluntarily increasing NSW can increase ML stability [117], though possibly at the cost of metabolic efficiency [45]. The variety of outcomes observed indicates that powered ankle exoskeletons have different impacts on ML stability across individuals, which should be considered further in the context of dynamic stability and potential for fall risk.

Gait characteristics can reveal independent changes in gait strategy components (i.e. AP and ML stability) early in exoskeleton use. We observed no strong correlations between changes in NSL and NSW prior to late phase de-adaptation despite previously discussed gait characteristic changes suggesting stabilizing strategies. It seems individuals initially altered gait characteristics independently to enable stability, e.g. a person may decrease NSL (AP stability) while not changing NSW (ML stability). One correlation between change in NSL and NSW was observed after five minutes of de-adaptation relative to baseline (D2-B3) – individuals that decreased NSL tended to increase NSW. Thus, relationships between NSL and NSW emerged across individuals around 20 minutes of exoskeleton walking, indicating a gait strategy prioritizing stability [116, 27, 38] in late phase de-adaptation. However, relationships between gait characteristics may emerge sooner with explicit instruction provision. For example, by instructing participants to modify NSW, Donelan [45] observed that individuals optimize NSW for minimal metabolic cost during 10 minutes of treadmill

92 walking. Varying task goals, i.e. instructions to use relaxed versus conservative gait, also show associated changes in NSL, NSW, and metabolic cost within 10 minutes [121]. Explicit task goals may yield correlations between gait characteristics by impacting the exploration space. The present open-ended task did not constrain initial strategy exploration, enabling participants to change NSL and NSW independently. Task instruction is an important independent variable to consider. Training protocols for exoskeletons should balance between goal-based instructions and allowance of strategy exploration.

To enable safe and effective exoskeleton operation, all factors which may impact gait strategy should be considered, including exoskeleton design, external perturbations, task goals, and training. In this study, exoskeleton operation impacted stability different across individuals, so further efforts should examine how interacting forces from the exoskeleton and environmental perturbations affect stability to address safety concerns like fall risk. Ad- ditionally, task goals can influence strategy selection. Goal-directed training methodologies could be developed to encourage desired behaviors in exoskeleton operators, just as exoskeleton training has been shown to support healthy movement patterns in patients post-stroke [8, 60]. For example, explicit instructions may initially encourage increased stability, then transition into strategies which minimize metabolic cost. Such training methods can enable safer, metabolically efficient adaptation to exoskeleton operation consistently across users.

Measures of NSL and NSW should be features of a set that informs user safety in exoskeleton training and operation. The present study measured exoskeleton gait characteristics without explicit strategy instructions during an initial exposure. The present study only examined 19 minutes of exoskeleton walking with 10 minutes of adaptation and five minutes of de-adaptation. Extended time with the system within the same day and across multiple days may result in emergent strategies for longitudinal use cases. Future directions include exploring relationships between different gait characteristics, varying instructions, and metabolic efficiency. Such work could inform training that quickly enables reductions in metabolic cost, i.e. by directing operators to increase NSL when the system is first powered on rather than allowing free strategy exploration. Such pertinent task goals can be incorporated into training to enable effective exoskeleton operation.

93 5.5 Conclusion

This chapter provides the first explicit presentation of individualized variation in exoskeleton gait characteristics, specifically as measured by normalized stride lengths and widths, when people are initially exposed to a powered ankle exoskeleton. This variation shows that individuals select different types of stabilization strategies in response to different walking conditions, e.g. when exoskeleton power is turned on or off, or over the course of adaptation or de-adaptation. This individualized variation will be incorporated as outcome parameters in Chapter 6. These exoskeleton parameters will be compared to baseline cognitive factors to evaluate whether those cognitive factors underlie the individualized exoskeleton gait variation observed in this chapter.

94 Chapter 6

Relationships between Cognitive Factors & Exoskeleton Gait

We have shown that there is significant individualized variation in human gait when utilizing an ankle exoskeleton system for the first time (Section 5.3.1). This individual variation is maintained after nearly 20 minutes of experience walking in the system. Reasons behind this individual variation are not well understood and may stem from endogenous factors such as differences in inhibitory control or ability to perform tasks with divided attention. We probed aspects of these cognitive factors in the HESA protocol (Chapter 3) and observed those same individuals’ exoskeleton gait characteristics in Chapter 5. This chapter compares probes of cognitive function and gait strategy to evaluate what relationships may exist with exoskeleton gait characteristics. Specifically, relationships with reaction times measures from the modified Simon task probing inhibitory control are explored. Gait characteristic and task performance measures with the self-paced treadmill (SPT) system are also included as probes of attention and gait with a TCLE system. Subsets of these data have already been presented and discussed in Chapter 4. Chapter 5 discussed observed variation in individuals’ gait characteristics during initial exoskeleton operation. Here we present aspects of these two data sets in combination with results from the modified Simon task to assess what, if any, relationships exist between modified Simon task and SP/SPDT parameters (referred to from here on as baseline parameters) and initial exoskeleton walking characteristics.

95 6.1 Cognitive Factors and Physical Activity

Individualized differences in gait and task performance have been observed in the context of lower-extremity exoskeletons, both in previous literature (e.g. [90]), and explicitly presented in this thesis (Chapter 5). In the context of patients, these differences may arise from variability in type or extent of pathology, such as level of a spinal cord injury (i.e. sacral versus lumbar, which results in differential symptoms), which can impact individuals ability to learn and effectively operate a exoskeleton [104]. However, individualized variation in exoskeleton gait as observed by muscle activation patterns and joint angle profiles occurs even in healthy populations [90]. It remains unclear what factors underlie this observed variability, but could include cognitive factors such as executive function or attention.

Cognitive function and physical ability (PA) are interlinked, with lower physical activity being correlated to declines in executive function (EF) [46, 25]. Greater PA is generally associated with higher levels EF in both younger [76] and older adults [196, 182]. Reasons for this relationship are unclear. There is some evidence for a neurophysiological basis for these relationships - it may be that greater PA and higher levels of EF both enable greater neuroplasticity [93]. Clinical studies have also found that higher levels of PA increases brain volume in areas associated with executive processing [147], thereby increasing EF. Broadly, PA can be beneficial to EF during the course of aging by decreasing the riskof cognitive decline, i.e. dementia, in older age [158], or even enabling moderate improvements in cognitive function in older adults with memory problems (though these adults did not meet the clinical criteria for Alzheimer’s disease) [95].

While EF in the aforementioned studied is generally defined broadly (i.e. including aspects of working memory, verbal fluency, and attention) there are specific cases thathave shown relationships between PA and simple reaction time tasks. Greater PA has been associated with faster reaction times to a variety of cue modalities (visual/auditory cues) across healthy older and younger adults [84, 107, 82]. In particular, Hillman et al. found that individuals with higher levels of PA reacted faster to both congruent and incongruent cues during a Flanker task [76], another inhibitory control probe similar to the Simon task presented in Chapter 2. Hillman also found faster RTs on incongruent cues in older adults with greater physical activity, indicating that inhibitory control can be impacted by PA.

There is strong evidence that PA and EF are related, with higher levels of PA being

96 associated with better executive functioning. The question of whether this relationship is bidirectional (i.e. can higher levels of EF enable greater PA?) is less clear. In one study, individuals showing worse executive function (e.g. lack of ability to inhibit repeated responses) were predicted to have lower levels of physical activity over time [36]. However, more work is required to establish the bidirectionality of a relationship between EF and PA broadly.

Investigations into the relationship between EF and PA in the context of exoskeleton operation have been limited. For example, Bequette [15] found that addition of physical and cognitive loads has been shown to impact exoskeleton walking characteristics as well as secondary task performance. However, broader relationships between aspects of EF and individuals’ ability to operate lower-extremity exoskeletons is lacking, providing an opportunity to evaluate these relationships in more detail.

6.2 Balancing Gait Goals During Exoskeleton Use

There may be a number of varying walking goals individuals balance while walking with an exoskeleton or a different TCLE system. These goals include but may not be limited to metabolic efficiency, stability, coordination or fluency with the system, and accuracyor precision in stepping. Metabolic efficiency, discussed previously in this thesis, is theidea of walking with minimal energy expenditure, a goal that many exoskeleton systems are designed to optimize for [61, 122, 164]. Stability, also previously discussed, particularly with respect to exoskeleton operation in Section 5.1.2), is the goal of walking without falling. Coordination or fluency with the system can be defined as a well-synchronized meshingof actions [78] and can look like a person walking in tandem with the power provided by a system rather than fighting the system (e.g. as can be seen in Figure 6-1). Human-robot fluency is further discussed in Section 1.2. Finally, individuals may prioritize accuracyor precision in stepping with a system, for example, individuals who were better able to achieve target speeds on a self-paced treadmill (SPT) in Chapter 4 were placing a high priority on accuracy and precision with the self-paced treadmill. These five gait goals may be prioritized or de-prioritized in varying scenarios while operating an exoskeleton or other TCLE system, and differences in cognitive factors may indicate preference for one or more ofthesegait goals under specific sets of circumstances.

97 Figure 6-1: Example of lack of coordination or fluency with an exoskeleton. In the presence added power, if an individual activates muscles and plants their foot early, they are fighting the system rather than maintaining good coordination with the system.

Changes in gait goals when walking with an exoskeleton can be observed and investigated using gait characteristics like change in NSL and NSW. Direction of change in gait characteristics during specific goal prioritization may or may not be dependent on exoskeleton power state. Stability is an essential gait goal to ensure that one doesn’t fall while walking. Walk- ing literature without exoskeletons or TCLE systems shows that decreased stride lengths and a wider base of support enable greater stability [69, 116, 27, 38]. Stability is agnostic of exoskeleton power state - increased NSW and decreased NSL indicate prioritization of stability both when a system is powered on or off. Prioritization of coordination is dependent on exoskeleton power state, however. When the system is powered on, an increase stride lengths is required to enable coordination with the added power from the system, though stride widths may not need to change. When the system is powered off, however, coordination is undefined. Assuming that coordination with the system means that the operator is allowing added power from the exoskeleton to assist them, then increased stride lengths would also indicate prioritization of metabolic efficiency. Stance width may not needto change, though narrower stride widths have generally been found to be more metabolically efficient during walking [45]. When the system powers off, stride lengths should decrease to prevent the operator needing to provide greater power to maintain stride lengths during powered walking. Lastly, gait precision and accuracy may be required in specific walking scenarios. However, there are also operators reticent to allowing an external system to modulate their gait and there they may want to maintain their baseline gait characteristics.

98 Figure 6-2: Table of gait priorities and associated changes in NSL and NSW during exoskeleton on and off power states. Up and down arrows indicate increases and decreases, respectively. Side arrows indicate no change. Circled X indicates undefined.

In that case, we would expect to see no change in stride lengths or widths during either powered or unpowered walking with an exoskeleton. These relationships between gait goal prioritization and changes in NSL and NSW are summarized in Figure 6-2. This chapter aims to explore whether an individual’s innate capacity to accomplish reaction time or gait tasks, exercise inhibitory control, or complete tasks with divided or limited attention, are related to their observed exoskeleton gait characteristics and how those may relate to various gait priorities.

6.3 Methods

All participant information can be found in Section 3.2. This section focuses on the methods to assess intra- and inter-subject variability for a selection of baseline parameters measured during the modified Simon task and SP/SPDT protocols in the HESA study and follow-on assessment of correlations between those baseline parameters and exoskeleton gait parameters. An in-depth description of the methods for this portion of the study follows.

6.3.1 Baseline Parameters

A list and description of each baseline parameter can be found in Table 6.1. Reaction time (RT) measures from the modified Simon task provide information about individuals’ ability to perform a simple reactive task to tactile cues while the difference in congruent and incongruent reaction times provides a probe of individuals’ ability to exercise inhibitory control.

99 These measures were included to assess whether individuals’ ability to complete a simple RT task with alternative interaction modalities, i.e. tactile cues and lower-extremity responses, may be related to how individuals operate a lower-extremity exoskeleton. All measures from self-paced treadmill walking (SP) and the self-paced dual task (SPDT) quantify either how well individuals accomplish a novel and unfamiliar gait task, i.e. achieving given target speeds, in particular green time proportion (GTP) and measured speed bias (Bias); or how those metrics quantify individuals gait strategy with the SPT, particularly normalized stride length (NSL), normalized stride width (NSW), and stride time (ST). These SP/SPDT measures were included to assess what types of motor interactions individuals have with a SPT, another TCLE system, and how those interactions may be related to individuals’ exoskeleton gait characteristics. Dual task accuracy (last parameter listed in Table 6.1) provides a measure of how well individuals perform a tactile and visual reaction time task while walking on a SPT, providing a measure of task performance under divided attention. Secondary task performance is especially relevant to exoskeleton walking characteristics in operational environments when secondary tasks during ambulation are commonplace. To further assess aspects of attention, differences in SPT parameters must be calculated with and without a dual task, i.e. the parameter value during SPDT subtracted by the parameter value during SP, termed dual task decrement (DTD). Further information on DTD calculations are found in Section 6.3.3. DTD of these parameters provides measures of how individuals’ gait task performance and characteristics change under divided attention, which again may be related to exoskeleton gait characteristics and is especially relevant for functional use in operational environments.

6.3.2 Intra- versus Inter-subject Variability

For a measure to discriminate between people, there must be a wide population spread with smaller individual spreads. We propose a method to identify cognitive and gait function probes that show high inter-subject variability but lower intra-subject variability. Measures that satisfy this criteria may be able to differentiate amongst different individual strategies and therefore could be used as predictors of individualized gait strategy characteristics. This approach is similar to approaches taken in identifying echo-cardiogram (ECG) measures useful in distinguishing between individuals. To utilize ECG measures to identify individuals, researchers measured population (i.e. inter-subject) spread of parameters via

100 Table 6.1: Table enumerating outcome variables from the modified Simon task and SP/SPDT trials associated with the exoskeleton walking protocol.

Task Parameter Description Congruent Reaction time for a tactile cue via the lower reaction time extremity (foot-tap) Incongruent Reaction time for a tactile cue via the lower Modified reaction time extremity (foot-tap) with spatial interference Simon Estimated cognitive processing time required Difference in task to overcome spatial interference in a tactile cue- Con/Incon RT lower extremity interaction mode A measure of gait task performance - proportion Green time of time within a given phase that was spent in the proportion target green region A measure of gait task performance - accuracy of Measured speed achievement on a self-paced treadmill given speed bias certain target speeds Normalized A measure of gait task performance - how quickly rise time individuals were able to reach a given target speed Normalized A gait characteristic while achieving target speeds Self-paced stride length during self-paced walking treadmill Normalized A gait characteristic while achieving target speeds walking stride width during self-paced walking A gait characteristic while achieving target speeds Stride time during self-paced walking A measure of gait task performance in the presence Green time of a secondary cognitive task - proportion of time proportion within a given phase that was spent in the target green region A measure of gait task performance in the presence Measured of a secondary cognitive task - accuracy of speed speed bias achievement on a self-paced treadmill given certain target speeds A measure of gait task performance in the presence Normalized of a secondary cognitive task - how quickly rise time individuals were able to reach a given target speed A gait characteristic while achieving target speeds Normalized during self-paced walking in the presence of a stride length secondary cognitive task A gait characteristic while achieving target speeds Normalized during self-paced walking in the presence of a stride width secondary cognitive task A gait characteristic while achieving target speeds Self-paced Stride time during self-paced walking in the presence of a dual tasking secondary cognitive task A measure of secondary task performance - how Dual task accurately individuals responded to secondary visual accuracy and tactile go/no-go cues while simultaneously achieving and maintaining target speeds

101 coefficient of variation (COV) and intra-subject repeatability using percent mean absolute difference (PMAD) between two measurements taken a year apart. One criteria used tofind distinctive measurements was low ratio of PMAD to COV [86]. However, data measured in the present study included more than two intra-subject measurements, so this method had to be adapted slightly. Another ECG study examined intra- and inter-subject variability in relationships between two ECG parameters [10]. This study determined differences between intra- and inter-subject variability of this relationship by comparing standard deviations of linear regression coefficients run within-subject to coefficients for a population regression using a Wilcoxon test. The present proposed method combines these two ideas.

To determine which parameters in Table 6.1 may be promising candidates as predictors of exoskeleton gait characteristics, parameters were tested for high inter-subject variability but different intra-subject variability. Inter-subject variability was compared to intra-subject variability using a combination of standard deviations and ratios between intra- and inter- subject spread. Population standard deviation (calculated using data across all subjects for a given parameter) was compared to within-subject standard deviations. A Wilcoxon signed rank test was conducted to evaluate if subject standard deviations were significantly different from the population standard deviation. Baseline parameters which showed significant differences in intra- and inter-subject variation were carried over to the nextstep.

Each baseline parameter in Table 6.1 was tested using the aforementioned method with the exception of difference between congruent and incongruent RT(ΔRT) from the modified Simon task. This parameter was calculated by taking the difference of mean congruent RT

(RTcon) and mean incongruent RT (RTincon), resulting in a single value per subject. Thus subject standard deviations for ΔRT could not be calculated.

It was desired to assess whether exoskeleton gait characteristics could similarly be utilized to differentiate amongst individuals. Two different types of exoskeleton gait characteristic measures were presented in Chapter 5 - observed values of NSLexo and NSWexo across all exoskeleton walking conditions (e.g. Figure 5-2) and six different values for change in NSL and NSW, ΔNSL and ΔNSW, across selected walking conditions (Figures 5-3 and 5-4).

Intra- versus inter-subject variability and spread ratios were also calculated for NSLexo and

NSWexo while walking at 1.3 m/s across all exoskeleton power walking conditions. Wilcoxon sign rank tests were conducted in the same way as previously described for both NSLexo and

NSWexo as for the baseline parameters. Because there was only one value per subject for

102 each ΔNSL and ΔNSW, this method could not be applied to those gait characteristics.

6.3.3 Relationships Between Baseline and Exoskeleton Gait Parameters

Changes in NSL and NSW (ΔNSL and ΔNSW) across selected exoskeleton walking conditions as presented in Section 5.3.1, rather than raw measures of NSL and NSW, were chosen as exoskeleton gait strategy parameters. Comparisons to ΔNSL and ΔNSW enabled operationally relevant interpretations relevant to the individual variability discussed in Section 5.4. Because these metrics were differences in mean NSL and NSW, each of these measures yielded only one value per subject. Each baseline metric, however, yielded many values per subject, thus requiring calculation of summary measures. These summary parameters were calculated only for baseline parameters that showed significantly different intra-subject variability from inter-subject variability. Summary parameters for the modified Simon task metric (mean RTs for congruent,

RTcon, and incongruent stimuli, RTincon) were averaged for each participant. ΔRT for each subject was calculated as the difference between these means. Two types of summary measures were calculated for SP and SPDT parameter data: means and dual task decrements (DTD). It was necessary to consider that SP/SPDT data was collected across a dimension not found in ΔNSL and ΔNSW for exoskeleton gait, namely at four different walking speeds (0.5, 1.0, 1.3, and 1.5 m/s), whereas exoskeleton gaitpa- rameters were collected only for a walking speed of 1.3 m/s. Vasudevan & Bastian [194] found that motor adaptation at slower speeds resulted in larger aftereffects that lasted longer than washout at faster speeds. We also found that dual tasking and gait characteristics were found to be significantly impacted by walking speed during the SP/SPDT protocols (Section 4.5). This differential effect on gait strategy with the SPT at varying walking speeds means that, in the present study, values of DTD between SP/SPDT at different speeds provide distinct information about individuals’ ability to complete novel or unfamiliar gait tasks under divided attention and higher workload conditions. It was desired that these nuances in cognitive ability be explored in relation with exoskeleton gait characteristics. For example, small differences in GTP were found at 1.3 m/s while larger differences were found at0.5m/s (Table 4.1). Individuals who showed larger decreases in GTP during slower walking speeds may possess less ability to perform complex or unfamiliar gait tasks under greater attentional loads, which may in turn be related to individuals’ exoskeleton gait characteristics.

103 Therefore, summary measures were calculated using different speed subsets of SP/SPDT data. Four mean parameters were calculated - across the entirety of SP and SPDT trials incorporating data from all speeds (휇(parameter)all); and using data only from trials with a walking speed of 0.5, 1.3, and 1.5 m/s (휇(parameter)0.5, 휇(parameter)1.3, 휇(parameter)1.5). Four DTD (i.e. difference in value of a given parameter between SP and SPDT) parameters were calculated, one for mean values across speeds, as well as three within specific speeds, i.e.

0.5, 1.3, and 1.5 m/s (e.g. DTD(parameter)all or DTD(parameter)1.5). The slowest, fastest, and matching exoskeleton walking speeds were chosen to account for a range of potential differences in mean and DTD values due to walking speed.

Each of the aforementioned summary measures provides insight into different cognitive factors, task performance capabilities, and gait characteristics with TCLE systems that may underlie individualized variation in exoskeleton gait. RTcon and RTincon from the modified Simon task provide a measure of individuals’ task performance ability using alternative interaction modalities while ΔRT provides information about individuals’ inhibitory control. SP/SPDT mean measures provide information about individuals’ ability to perform a novel or unfamiliar gait task (mean GTP and Bias measures) or their gait characteristics with the SPT (mean NSW measures). DTD measures, on the other, provide insight into individuals’ attention and how they change task performance or gait characteristics under divided attention.

Ultimately, 12 exoskeleton gait parameters were calculated, ΔNSL and ΔNSW for each of six walking condition pairs: B3A1 (exoskeleton power off → on), A3D1 (exoskeleton power on → off), A1A3 (change over the course of adaptation), D1D2 (change overthe course of de-adaptation), B3A3 (adapted state relative to baseline) and B3D2 (de-adapted state relative to baseline). Three modified Simon task parameterscon (RT , RTincon, ΔRT) were calculated as baseline parameters. Eight summary measures were calculated for each of three SP/SPDT baseline parameter passing the variability criteria in Section 6.3.2 - four means (휇()all, 휇()0.5, 휇()1.3, 휇()1.5); and four DTD values (DTD()all, DTD()0.5, DTD()1.3, and DTD()1.5) for each significant SP/SPDT parameter. To assess relationships between calculated summary baseline parameters and exoskeleton gait strategy parameters, Pearson and Spearman’s rank correlation coefficients (r and 휌, respectively) were calculated for each combination of baseline parameter and exoskeleton gait strategy parameter. Statistically significant훼 ( < 0.05) Pearson correlation coefficients de-

104 noted linear relationships between parameters while significant Spearman’s rank correlation coefficients denoted non-linear monotonic relationships between parameters. No correction for multiple comparisons was made because the present goal was to find a variety of baseline parameters that may be related to or be predictive of exoskeleton gait parameters, not to make specific claims regarding the relationships themselves. We acknowledge a greater possibility of Type 1 error. This possibility was allowed to enable identification of a wide selection of baseline parameters that may be promising candidates in future work.

6.4 Results

6.4.1 Intra- vs. Inter-subject Variability

A selection of parameters from both the modified Simon task and the SP/SPDT experiments were found to be promising predictor candidates based on ability to distinguish between subjects. Both congruent and incongruent response times were found to show significant differences in intra- and inter-subject variability, as did GTP, speed bias, and NSWduring SP (Table 6.2). GTP and NSW during SPDT were also found to show significantly different intra- and inter-subject variability. Additionally, a subject to population standard deviation ratio of approximately 0.8 and below was found for all parameters that showed differences in population and subject standard deviations. Exoskeleton gait characteristics showed significantly different intra- versus inter-subject variability as well (NSL: Z = -3.41, p < 0.001; NSW: Z = -3.24, p = 0.001). Exoskeleton gait characteristics also showed low ratios of intra-subject to inter-subject spread (NSL ratio = 0.442, NSW ratio = 0.528).

6.4.2 Baseline and Exoskeleton Gait Parameter Correlations

Calculations of correlation coefficients between 12 exoskeleton gait parameters and 27base- line parameters (three modified Simon task parameters and eight summary measures ofeach GTP, Bias, and NSW during SP/SPDT) indicate that some baseline parameters investigated here may be correlated to exoskeleton gait parameters. 33 significant pairings were found (Table 6.3), 25 of which contained ΔNSL as the exoskeleton metric. Of the 33 significant correlations, 13 were during exoskeleton de-adaptation (walking condition comparison D1D2) and seven at late exoskeleton de-adaptation relative to baseline (walking condition compar-

105 Table 6.2: Assessment of intra- versus inter-subject variability for all baseline parameters listed in Table 6.1. Results of Wilcoxon sign-rank tests on subject standard deviations compared to population standard deviation are presented. Parameters with statistically significant differences in intra- versus inter-subject variability are denoted with anaster- isk. Ratios of mean subject standard deviation to population standard deviation are also reported.

Baseline Task Parameter p-value Sign Rank StDev Ratio Congruent RT 0.026* 21 0.776 Modified Simon Task Incongruent RT 0.010* 16 0.806 GTP 0.001* 5 0.662 Speed Bias 0.018* 19 0.763 NRT 0.135 33 0.908 Self-paced NSL 0.151 34 0.944 NSW < 0.001* 0 0.646 ST 0.599 50 0.983 GTP 0.001* 1 0.677 Speed Bias 0.380 27 0.896 NRT 0.151 20 0.836 Self-paced dual task NSL 0.077 16 0.915 NSW < 0.001* 0 0.573 ST 0.301 25 0.953 DT Accuracy 0.910 24 0.998 ison B3D2). Breakdown of the 33 pairs with respect to baseline parameters indicated that many different types of baseline parameters may be related to exoskeleton gait characteristics. Significant pairings included eight with a modified Simon task parameter, eightwith DTD values between SP/SPDT, and 17 mean values (all but one being either GTP or Bias means). This indicates that individuals’ exoskeleton gait characteristics may be related to various aspects of individuals’ inhibitory control, ability to complete dual tasks, and ability to achieve target speeds on a self-paced treadmill. The following sections detail correlation results grouped by baseline parameters for a selection of correlations. Scatter plots for all correlations found to be significant are presented in Appendix A.3.

Reaction time baseline parameters from the modified Simon task

Individuals with slower RTs showed monotonically decreasing ΔNSL when the exoskeleton was first powered on (Figure 6-3a) and over the course of de-adaptation (Figures 6-3band 6-3e). These same individuals also tended to show monotonic increases in ΔNSW when exoskeleton power was added (Figure 6-3d) and after five minutes of de-adaptation relative

106 Table 6.3: Baseline and exoskeleton metric comparisons with significant Pearson’s r or Spear- man’s 휌. Baseline metrics are all summary measures of the parameters from Table 6.2 which showed significance. Bias, GTP, and NSW across SP/SPDT are pooled in mean metrics (e.g. 휇(Bias)all, 휇(GTP)1.3) while DTD metrics are the difference in mean value across SPand SPDT. Significant p-values at an 훼 level of 0.05 are bold.

Pearson Spearman Exo Metric Baseline Metric r 95% CI p-value 휌 p-value 1 ΔNSLB3A1 RTcon -0.327 [-0.719, 0.223] 0.234 -0.564 0.031 2 ΔNSLB3A1 휇(GTP)all -0.526 [-0.818, -0.019] 0.044 -0.457 0.089 3 ΔNSLB3A1 휇(GTP)1.3 -0.606 [-0.853, -0.136] 0.017 -0.543 0.039 4 ΔNSLA3D1 ΔRT 0.580 [0.097, 0.842] 0.023 0.429 0.113 5 ΔNSLA3D1 휇(Bias)all -0.335 [-0.723, 0.214] 0.223 -0.536 0.042 6 ΔNSLA1A3 ΔRT -0.556 [-0.831, -0.061] 0.031 -0.404 0.137 7 ΔNSLA1A3 DTD(Bias)0.5 -0.684 [-0.903, -0.181] 0.014 -0.455 0.140 8 ΔNSLA1A3 DTD(Bias)1.3 -0.268 [-0.730, 0.361] 0.400 -0.594 0.046 9 ΔNSLD1D2 RTcon -0.456 [-0.785, 0.074] 0.088 -0.539 0.041 10 ΔNSLD1D2 RTincon -0.461 [-0.787, 0.067] 0.084 -0.618 0.016 11 ΔNSLD1D2 DTD(NSW)all -0.598 [-0.873, -0.037] 0.040 -0.014 0.974 12 ΔNSLD1D2 휇(GTP)all -0.648 [-0.871, -0.204] 0.009 -0.550 0.036 13 ΔNSLD1D2 휇(GTP)1.3 -0.573 [-0.839, -0.086] 0.026 -0.546 0.038 14 ΔNSLD1D2 DTD(Bias)all 0.580 [0.009, 0.866] 0.048 0.238 0.457 15 ΔNSLD1D2 DTD(Bias)0.5 0.592 [0.028, 0.870] 0.042 0.161 0.619 16 ΔNSLB3A3 DTD(Bias)1.3 -0.617 [-0.880, -0.067] 0.032 -0.406 0.193 17 ΔNSLB3D2 휇(GTP)all -0.576 [-0.840, -0.090] 0.025 -0.557 0.034 18 ΔNSLB3D2 휇(GTP)1.3 -0.657 [-0.875, -0.219] 0.008 -0.679 0.007 19 ΔNSWB3A1 RTcon 0.451 [-0.079, 0.783] 0.091 0.532 0.044 20 ΔNSWD1D2 ΔRT 0.591 [0.112, 0.847] 0.020 0.593 0.022 21 ΔNSWD1D2 DTD(NSW)all 0.595 [0.032, 0.871] 0.041 0.538 0.075 22 ΔNSWD1D2 DTD(NSW)0.5 0.625 [0.079, 0.882] 0.030 0.643 0.028 23 ΔNSWB3D2 RTincon 0.457 [-0.072, 0.786] 0.087 0.543 0.039 24 ΔNSLB3A1 휇(Bias)0.5 0.407 [-0.133, 0.761] 0.132 0.536 0.042 25 ΔNSLB3A1 휇(GTP)0.5 -0.518 [-0.814, -0.008] 0.048 -0.514 0.052 26 ΔNSLB3A1 휇(NSW)0.5 0.360 [-0.186, 0.737] 0.187 0.636 0.013 27 ΔNSLD1D2 휇(Bias)0.5 0.688 [0.271, 0.887] 0.005 0.514 0.052 28 ΔNSLD1D2 휇(GTP)0.5 -0.764 [-0.917, -0.414] 0.001 -0.729 0.003 29 ΔNSLB3D2 휇(Bias)0.5 0.596 [0.121, 0.849] 0.019 0.643 0.012 30 ΔNSLB3D2 휇(GTP)0.5 -0.570 [-0.838, -0.082] 0.026 -0.589 0.023 31 ΔNSWD1D2 휇(GTP)1.5 0.516 [0.005, 0.813] 0.049 0.454 0.092 32 ΔNSWB3D2 휇(Bias)0.5 -0.588 [-0.846, -0.109] 0.021 -0.418 0.123 33 ΔNSWB3D2 휇(GTP)0.5 0.626 [0.167, 0.862] 0.013 0.457 0.089

107 to exoskeleton baseline, i.e. before the system was powered on (Figure 6-3c). Individuals who showed larger ΔRT also showed decreased ΔNSL over the course of exoskeleton adaptation and increased ΔNSW during de-adaptation (Figures 6-3g and 6-3h), though these two were linear relationships rather than monotonic.

SP/SPDT mean baseline parameters

Individuals with greater mean bias or lower mean GTP at a target speed of 0.5 m/s tend to increase ΔNSL when the exoskeleton is first powered on (Figures 6-4a and 6-4d), during de-adaptation (Figures 6-4b and 6-4e), and during late de-adaptation relative to exoskeleton baseline (Figures 6-4c and 6-4f). All of these were found to be linear relationships with the exception of the correlation between ΔNSLB3A1 and 휇(Bias)0.5, which was monotonically increasing. These same individuals showed linearly decreasing ΔNSW during late de-adaptation relative to exoskeleton baseline (Figures 6-4g and 6-4h).

SP/SPDT dual task decrement baseline parameters

Individuals that showed increased DTD in Bias between SP and SPDT at a target speed of 0.5 m/s showed decreased ΔNSL when exoskeleton power was added (Figure 6-5a) and increased ΔNSL over the course of de-adaptation (Figure 6-5b).

6.5 Discussion

The goal of this study was to select a set promising predictors of exoskeleton gait characteristics from cognitive baseline parameters. We found that a number of parameters relating to inhibitory control and reaction time performance (i.e. measures from the modified Simon task) as well as aspects of attention quantified by task performance and gait characteristics during a dual task paradigm (i.e. mean and DTD measures of GTP, Bias, and NSW in the SP/SPDT protocol) are related to exoskeleton gait parameters. These parameters should be further investigated as potential predictors of gait characteristics and strategy during exoskeleton operation. Here we discuss findings related to intra- versus inter-subject variability for different parameters and implications for predictor candidacy. We expand upon pairings between a selection of exoskeleton gait and baseline parameters that showed significant correlations and discuss potential interpretations and implications for exoskeleton

108 (a) ΔNSLB3A1 vs. RTcon (b) ΔNSLD1D2 vs. RTcon (c) ΔNSWB3D2 vs. RTincon

(d) ΔNSWB3A1 vs. RTcon (e) ΔNSLD1D2 vs. RTincon (f) ΔNSLA3D1 vs. ΔRT

(g) ΔNSLA1A3 vs. ΔRT (h) ΔNSWD1D2 vs. ΔRT

Figure 6-3: Correlations between baseline parameters measured from the Simon task (RTcon, RTincon, and ΔRT) and exoskeleton gait parameters.

109 (a) ΔNSLB3A1 vs. 휇(Bias)0.5 (b) ΔNSLD1D2 vs. 휇(Bias)0.5 (c) ΔNSLB3D2 vs. 휇(Bias)0.5

(d) ΔNSLB3A1 vs. 휇(GTP)0.5 (e) ΔNSLD1D2 vs. 휇(GTP)0.5 (f) ΔNSLB3D2 vs. 휇(GTP)0.5

(g) ΔNSWB3D2 vs. 휇(Bias)0.5 (h) ΔNSWB3D2 vs. 휇(GTP)0.5

Figure 6-4: Correlations between mean GTP and Bias baseline parameters measured at 0.5 m/s during SP/SPDT and exoskeleton gait parameters.

110 (a) ΔNSLA1A3 vs. DTD(Bias)0.5 (b) ΔNSLD1D2 vs. DTD(Bias)0.5

Figure 6-5: Selected correlations between dual task decrement (DTD) Bias parameters measured across SP/SPDT and exoskeleton gait parameters. use. Finally, we discuss limitations and future work relating to cognitive factors underlying individualized variation in exoskeleton gait characteristics and strategy.

Select measures of cognitive function, specifically those which probe aspects of inhibitory control and dual task performance, can be used to distinguish between individuals. Foot- tap response times to congruent and incongruent stimuli presented using tactile cues on the lower extremity showed significantly different intra-subject variability than the population spread, indicating that this RT measure may able to differentiate amongst individuals. Similarly, measures of time individuals spent within a desired target speed region (GTP), how far off their mean speed was from the target speed (Bias), and their NSWduringthe SP/SPDT protocols also showed significantly different intra-subject spread than the population spread. Normalized stride length, stride time, and dual task accuracy did not show significantly different individual versus population spreads. NSL and ST both have aclear linear relationship with walking speed [66], while NSW does not. While Bias and GTP were impacted by speed, that relationship was non-linear. NSL and ST have a linear relationship with speed, meaning that changes NSL and ST with speed were likely consistent across individuals, resulting in intra-subject variability that was not different than inter-subject variability and therefore unable to differentiate amongst individuals. Spread ratios for NSL and ST were above 0.9 for both SP and SPDT, further indicating that individual spread was similar to population spread. Similarly, dual task accuracy did not distinguish between individuals in the current sample. Measures that showed significantly different intra-subject

111 variability relative to inter-subject variability also showed lower ratios of subject to population spread than measures not found to be significant. It is noted that the population in the current study consisted of college-age adults with no walking impairments, thus, finding parameters that distinguish between individuals is an encouraging sign that baseline cognitive predictors of exoskeleton gait strategy likely exist. However, extending this study to a wider population could identify additional measures as candidate predictors not apparent in this sample.

Present data indicate that ΔNSL across various phases of exoskeleton walking is more closely correlated to the baseline parameters tested here than ΔNSW. Most (25 of 33) significant correlations were found to be with ΔNSL across all six different exoskeleton walking condition pairs (Table 6.3). This seems counter-intuitive because one would assume that both NSLexo and NSWexo would show correlations with baseline parameters given that both were found to be good differentiators as evidenced by significantly different intra- versus intra-subject variability and low subject to population standard deviation ratios. However, it may be that individuals are more likely to consciously modulate NSL rather than NSW while walking under perturbed or off-nominal conditions. For example, when instructed to walk with risky versus conservative gait strategies, individuals showed significant changes in stride length but not stride width [121]. This greater likelihood of conscious modulation may by why more correlations were found with exoskeleton ΔNSL than ΔNSW. It is also possible that self-paced treadmills and exoskeletons, while both TCLE systems, impact gait differently. Walking with an exoskeleton on a fixed-paced treadmill may impact NSL moreso than walking on a self-paced treadmill without an exoskeleton. Individuals have been found to increase SL on a fixed-pace treadmill to maintain speed while not changing SL on aself- paced treadmill [174], a phenomenon that may be manifesting in the present comparisons between fixed speed exoskeleton walking and self-paced treadmill walking. Future work should explore how gait characteristics change when an exoskeleton is operated on a self- paced treadmill system to further disentangle this confound.

Changes in gait characteristics during exoskeleton de-adaptation seem to be a promising potential target for prediction using the present baseline parameters, as 14 out of 33 significant correlations were found to be with gait characteristics during de-adaptation (ΔNSLD1D2 and ΔNSWD1D2) and seven were related to gait characteristics during late de-adaptation relative to baseline (ΔNSLB3D2 and ΔNSWB3D2). There is much work examining how in-

112 dividuals adapt to exoskeleton power addition and changes over time (e.g. [65, 61, 90, 83]). However, exoskeleton gait and adaptation studies generally do not examine in detail the de- adaptation process, rather, a fraction of total adaptation time is allowed for de-adaptation. It is unclear how exoskeletons may impact gait characteristics longitudinally, even after being powered off for de-adaptation. Though relatively small changes in gait characteristics were observed after five minutes of de-adaptation, gait characteristics at the end of fivemin- utes of de-adaptation were often significantly different from individuals’ baseline exoskeleton gait strategy, i.e. prior to exoskeleton power addition (Section 5.3.1), indicating that components of gait strategy do not wash out (return to baseline) within five minutes of exoskeleton power down. Lack of washout can be observed in motor adaptation studies with split-belt treadmills as well [111], which indicates that individuals can retain new gait strategies even when no longer operating a TCLE system. This retention can also be operationally useful, for example, to help train patients to walk with desired gait characteristics post-stroke [156, 154], enabling these patients to walk with more gait symmetry. It is possible that exoskeleton operation can also lead to retention of new gait strategy characteristics during de-adaptation. This possibility should be further explored to better assess exoskeleton use even after the system is powered off, as that knowledge can be used to inform exoskeleton training protocols or encourage desired behaviors during or following exoskeleton operation.

Individuals who respond more quickly to tactile stimuli (both congruent and incongruent) with their lower extremities may prioritize coordination with the exoskeleton over stability. Faster RTs were found to be correlated with greater ΔNSL and lower ΔNSW, which indicate lower anteroposterior (AP) and mediolateral (ML) stability, respectively [68, 116, 27, 38]. It may be that individuals who respond more quickly to stimuli are less likely to be cautious when operating an exoskeleton. Individuals with greater impulsivity have been shown to have faster reaction times [50], and greater impulsivity may be why individuals do not select cautious gait strategies, i.e. those that prioritize stability. These individuals may have greater confidence in their ability to respond quickly to an off-nominal state or destabilizing perturbation during exoskeleton operation and therefore prioritize coordination with the system, i.e. allowing added power from the system to assist them by increasing their NSL (Figure 6-3a) and decreasing NSL over the course of de-adaptation (Figure 6-3b and 6-3e). The same may be true for individuals who are more quickly able to exercise inhibitory control, i.e. those with lower ΔRT. However, present results paint a conflicting picture of

113 correlations between ΔRT and exoskeleton gait, with increasing NSL indeed observed during adaptation (Figure 6-3g), but decreasing NSL seen when exoskeleton power is first added (Figure 6-3f). It should also be noted these two correlations may be driven by one participant outlier, without whom the correlations may not be significant. The five relationships with

RTcon and RTincon, however, are in agreement. For those reasons, it may be that inhibitory control is less predictive of exoskeleton gait, but task performance during the Simon task, i.e. reaction times, is related to individualized exoskeleton gait characteristics. Individuals who react more quickly to tactile cues with their lower extremity tend to prioritize coordination with the system while walking with an exoskeleton over stability, possibly due to greater confidence in their ability to quickly react to unexpected exoskeleton perturbations.

Individuals who are less able to achieve target speeds under higher attentional load may de-prioritize gait accuracy or stability in favor of greater coordination with TCLE systems. Maintaining a target speed of 0.5 m/s on a SPT has been shown to require greater attention as evidenced by lower task performance (Figure 4-2) and greater DTD (Table 4.1) in speed bias (speed ratio in the referenced figure and table), GTP, and speed COV. It may be that individuals who show greater bias and lower GTP allow the system to adjust their movements rather than force a precise walking pattern. These same individuals tend to increase NSL when exoskeleton power is first added (Figures 6-4a and 6-4d). An increase in NSLwith exoskeleton power addition indicates that individuals are coordinating with the system to allow it to lengthen their strides. This greater coordination may, however, be accompanied by decreases in stability with the exoskeleton, as these same individuals show lower AP and ML stability with the exoskeleton as observed by increases in NSL and decreases in NSW during de-adaptation and by late de-adaptation relative to exoskeleton baseline (Figure 6-4b,c,e-h). Consciously increasing SW or decreasing SL can enable greater stability [117], and external strategy provision requiring conscious modulation toward a more conservative gait strategy led individuals to shorten NSL [121]. However, these changes require directed attention towards gait modulation. When completing a dual task under conditions in which executive functions like attention may be compromised, i.e. with a concussion, individuals utilized less stable gait characteristics [26, 138]. Present data shows a similar pattern - being less able to achieve slow target speeds (i.e. under divided attention) is correlated to decreased stability with an exoskeleton. Despite this decrease in stability however, no falls were observed, indicating that this decreased stability was not operationally relevant for the young, healthy

114 participants in the present study. Thus, participants in the current population were safely able to prioritize coordination with the exoskeleton over stability. However, this result may not necessarily transfer to other populations, such as elderly adults or those with gait pathologies, who have an increased fall risk at baseline and might be unable to walk safely with the less stable gait characteristics observed here. Therefore, these relationships between attention and stability or coordination with an exoskeleton should be further investigated to assess how cognitive factors differently impact the gait of individuals who may have underlying gait abnormalities or pathologies.

Relationships between individuals’ DTD(Bias)0.5 during SP/SPDT and exoskeleton gait characteristics show that individuals who are less able to achieve target speeds with the addition of a dual task (greater DTD) seem to utilize exoskeleton gait strategies contrary to the system goals. These individuals show decreasing NSL over the course of exoskeleton adaptation (Figure 6-5a), indicating they are fighting the system. They do not allow added power from the system to lengthen their strides, rather they place their foot on the ground earlier in opposition to added power. Given that increased cognitive loads can lead to decremented task performance [15], perhaps individuals who are less able to achieve target speeds during a novel or unfamiliar gait task in the presence of a secondary task select exoskeleton gait strategies that enable greater AP stability, which may ease interference from added cognitive load. The opposite pattern of ΔNSL is seen during de-adaptation, where these same individuals show increases in NSL (Figure 6-5b) when decreases would be expected due to power removal. This may mean individuals are more comfortable without added exoskeleton power, allowing them to select a gait strategy with lower AP stability. It must be noted that both these correlations are the result of an outlier, thus these comparisons should be further investigated prior to any strong conclusions being drawn. However, intuitively it seems plausible that individuals who are less able to operate a SPT as instructed under greater attentional load may also operate an exoskeleton contrary to desired goals, at least upon initial exposure to the system.

Present data show a number of potential correlations between exoskeleton gait and baseline cognitive parameters (summarized in Figure 6-6). These relationships are complex and should be rigorously investigated. While it is difficult (and perhaps unwise, given the greater possibility of Type 1 error) to ascertain the exact nature of these correlations from present data, it is clear that relationships between cognitive factors and exoskeleton gait strategy

115 Figure 6-6: Summary table of the possible relationships between baseline cognitive parameters and exoskeleton gait characteristics, with associated implications for shifting gait priorities. likely exist. The mix of modified Simon task, mean SP/SPDT, and DTD SP/SPDT parameters found to have significant correlations with exoskeleton gait parameters indicates that further research should examine these parameters in greater detail.

6.5.1 Limitations

The goal of this study was to identify baseline cognitive parameters as candidate measures that may be related to exoskeleton gait parameters. We do not attempt to make claims about the specific relationships between baseline and exoskeleton gait parameters here. Due to the relatively low sample size (n=15, or n=12 in some pairwise comparisons) and multiple comparisons without correction, the aforementioned results are preliminary. All observed correlations should be rigorously and repeatably tested for confirmation and further analysis. Additionally, the experimental setup was such that there was a dimension of walking speed in SP/SPDT data that did not exist for the exoskeleton gait parameters. However, due to significant effects of walking speed observed on individuals’ gait characteristics and ability to dual task on the SPT, it was desired to retain different walking speeds within calculated summary measures. Thus, walking speed may be a confounding factor. Further work should consider how relationships between baseline and exoskeleton gait parameters may be impacted by walking speed, especially given that dual task performance varies with different walking speeds as shown in Chapter 4.

116 6.6 Conclusion

There are clearly relationships between motor action and cognitive function. For example, higher levels of exercise are associated with older adults’ ability to complete a number of cognitive tasks, including those assessing memory and attention [25]. We have further shown that baseline cognitive parameters from a modified Simon task and self-pacing/self-paced dual tasking protocols show significant correlations with exoskeleton gait characteristics, namely change in NSL and NSW due to exoskeleton walking conditions (i.e. power on → off and vice versa, during 10 minutes of adaptation and five minutes of de-adaptation, and in adapted or de-adapted states relative to exoskeleton baseline). Present data indicate that ΔNSL and changes in either gait metric over the course of de-adaptation may be of special interest. Faster reaction times during the modified Simon task and less accurate achievement of slower target speeds may be indicative of individuals who prioritize coordination with the exoskeleton over gait stability during system use. Individuals with greater decrement in target speed achievement in the presence of a dual task, however, tended to show poor coordination with the exoskeleton. While the present data does not allow for strong assertions about the nature of these relationships between exoskeleton gait and baseline cognitive parameters, it is clear that these relationships likely exist. These results support the hypothesis that select cognitive factors that may underlie individualized variability in exoskeleton gait strategy, and this thesis has presented an evidence-based selection of baseline parameters that warrant further investigation.

117 118 Chapter 7

Conclusions & Future Work

The goal of this thesis was to evaluate human interaction with tightly-coupled lower-extremity systems (i.e. self-paced treadmills and lower-extremity exoskeletons) from a human factors perspective with particular focused on individualized variation. Any number of factors, both individual-specific and externally provided, may impact human-system interaction. This thesis explored the impact of a subset of cognitive factors on individuals’ interaction and task performance with TCLE systems with consideration for alternative interaction modalities, i.e. tactile cues and lower-extremity responses, enabled by the unique nature of TCLE systems. This thesis aimed to answer three questions in particular:

1. How do alternative interaction modalities impact peoples’ ability to complete goal- oriented tasks with human-machine systems?

2. How do tightly-coupled lower-extremity systems impact individuals’ gait strategy and ability to perform goal-oriented tasks?

3. What cognitive factors underlie individualized variation in gait strategy with lower- extremity exoskeletons?

Understanding the answer to these questions can inform an essential aspect of human- system interaction: cognitive fit. So, to answer these questions and provide an understanding of the factors that enable good cognitive fit across distinct users, this thesis set outto accomplish four specific aims:

1. Assess the effect of varying interaction modalities on a reaction time task.

119 2. Characterize human gait strategy while walking on a self-paced treadmill system with a dual task.

3. Evaluate the effects of a powered lower-extremity exoskeleton on human gait strategy.

4. Identify factors that may be related individualized human gait strategy during exoskeleton use.

The first aim was addressed in Chapter 2, with the extensions and validation ofain- hibitory control probe to alternative cue-response modalities relevant to TCLE system operation. A comprehensive human-exoskeleton strategy and adaptation study was designed to address Aims 2-4, germane portions of which were presented in Chapter 3. Chapter 4 ex- tracted a subset of data from the HESA study to address Aim 2, with the characterization of human gait and secondary task performance at varying speeds on a self-paced treadmill. Aim 3 was addressed in Chapter 5, with the first explicit presentation of variation in exoskeleton gait strategy across individuals. Data from chapters 3 - 5 were combined to address Aim 4 in Chapter 6, where correlations between baseline cognitive parameters and exoskeleton gait were explored.

7.1 Summary of Results

Here we provide a summary of the main results from each chapter in this thesis, then outline major contributions to the literature in the next section.

7.1.1 Human Cognition & Performance in Alternative Interaction Modes

Chapter 2 of this thesis investigated how alternative cue-response interaction modalities impacted task performance and inhibitory control. A commonly utilized probe of inhibitory control, the Simon task, was extended to cue-response modalities relevant to TCLE system use, namely tactile cues on the lower-extremity, with lower-extremity responses. The Simon effect, i.e. increased reaction times as a result of incongruent spatial presentation ofcues, was maintained across different cue-response modalities. However, individuals showed longer reaction times, as well as lower response rates and accuracy, with alternative interaction modalities. Thus, alternative interaction modalities can be utilized to probe inhibitory control, but may result in differential task performance.

120 7.1.2 The Human-Exoskeleton Strategy & Adaptation Study

Chapter 3 presented a comprehensive study protocol to enable assessment of what sensorimotor and cognitive factors may underlie individualized variation in exoskeleton gait characteristics. While this thesis analysed and presented results from a subset of the HESA study, a plethora of data on individuals’ sensory, cognitive, and motor abilities resulted from that study, enabling future investigators to delve more deeply into factors underlying exoskeleton gait strategy and adaptation.

7.1.3 Gait Strategy & Dual Tasking with a Self-paced Treadmill

In Chapter 4, a subset of data from the HESA study exploring individuals’ gait strategy and dual task performance on a self-paced treadmill (SPT) was presented. Results showed that individuals were able to quickly improve their ability to accomplish the instructed gait task - to achieve varying target speeds (0.5, 1.0, 1.3, and 1.5 m/s). Good gait task performance was shown by high levels of green time proportion (GTP) sustained following phase 1 of exposure to the SPT. However, individuals continued to refine aspects of their SPT use during speed transitions, showing a greater ability to achieve slow and fast target speeds more quickly (shorter rise times) during their second SPT trial. The chapter further presented the impact of different cue input modalities and walking speed on attention as observed by individuals’ ability to maintain target speeds and simultaneously complete a secondary task. Individuals were able to respond to visual cues similarly across speeds, but response accuracy to a tactile cue on the wrist that required shifting of visual attention was lower at slower target walking speeds. Walking speed and addition of a dual task also impacted individuals’ stability. Medio-lateral stability was decreased (narrowed normalized stride width, NSW) with the addition of a dual task, while antero-posterior stability as measured by normalized stride length (NSL) and stride time changed by only about 2% with the addition of a dual task, but showed changes of nearly 100% across walking speeds of 0.5 to 1.5 m/s.

7.1.4 Individualized Exoskeleton Gait Strategies

Chapter 5 examined individuals’ gait strategy and stability as observed via NSL and NSW, and how those gait characteristics changed across a number of exoskeleton power states

121 and over time. Consistency across individuals was observed during adaptation and de- adaptation, with most individuals increasing NSL and decreasing NSW during adaptation, and most people showing small, statistically insignificant changes in both gait characteristics during de-adaptation. However, individuals showed different NSL and NSW by late adaptation and de-adaptation relative to their own baselines. Further, variability in responses to addition and removal of exoskeleton power was observed, with various individuals showing increases, decreases, or no significant changes in NSL due to a power change. Individuals also shifted their lateral stability, i.e. NSW, differently when exoskeleton power was added or removed - only about half increased lateral stability via wider NSW upon power addition. A different set of approximately half of all individuals decreased NSW upon power removal.

7.1.5 Relationships between Cognitive Factors & Exoskeleton Gait

Chapter 6 explored relationships between baseline cognitive factors assessed during the HESA study and individual variation in exoskeleton gait characteristics presented in Chap- ter 5. First, intra- versus inter-subject variability of baseline factors was assessed, showing that reaction time measures from the Simon task and green time proportion (GTP), mean speed deviation from target speed (Bias), and normalized stride width (NSWSP) during the SP/SPDT experiments (Chapter 4) showed significantly different variation between individual and population spreads. Correlations between summary measures of these parameters that probed aspects of individuals’ inhibitory control, attention, and gait characteristics with a TCLE system showed significant relationships with changes in NSL andΔ NSW( NSLexo and ΔNSWexo) during exoskeleton gait. Evaluation of correlations indicated that ΔNSLexo is more likely related to aspects of cognitive function than ΔNSWexo and baseline cognitive factors may be indicative of individuals’ exoskeleton de-adaptation strategies. Addition- ally, individuals with faster reaction times tend to coordinate their motion with exoskeleton power changes by increasing NSL when power addition and decreasing NSL with power removal. Those who were less able to achieve slow target speeds on a self-paced treadmill also coordinated with the system with power addition, but they tended to de-priorize stability during de-adaptation. Finally, individuals that seemed less able to achieve target speeds under divided attention, i.e. with dual task addition, seemed to utilize the exoskeleton contrary to system goals as observed by decreased NSL with power addition but increased NSL with power removal.

122 7.2 Contributions

This thesis makes a number of contributions to the literature in human-system interaction and exoskeleton use.

1. The Simon effect, a probe of inhibitory control, is maintained across alternative cue- response interaction modes and extended implementation of the Simon task to utilize tactile cue presentation and lower-extremity responses.

2. Tactile cues and lower-extremity responses significantly impact task performance, in particular via increased reaction times, as well as decreased response accuracy and response rates.

3. Individuals are quickly able to achieve target speeds on a self-paced treadmill as observed by steady state measures, but continue to refine aspects of their transient strategy, i.e. how quickly they move between speeds, as they become more familiar with the system controller.

4. Walking at preferred walking speeds (PWS) requires less attention and individuals are better able to complete secondary tasks at PWS rather than at slower speeds.

5. Walking speed impacts anteroposterior (AP) stability (i.e. NSL and ST) more so than mediolateral (ML) stability (i.e. NSW), while addition of a dual task has a greater impact on ML stability than does speed.

6. This thesis presented details of individual variation in exoskeleton gait characteristics, finding that many people shift their spatiotemporal gait characteristics in similar ways over time during exoskeleton adaptation or de-adaptation, whereas changes in gait characteristics due to exoskeleton power state perturbations (i.e. on ↔ off) result in inconsistent gait changes across individuals.

7. This thesis presented a preliminary list of individual cognitive factors relating to inhibitory control, attention, and TCLE system use that may underlie individualized variation in exoskeleton gait.

8. Changes in NSL during exoskeleton gait are more likely related to the baseline cognitive factors explored in this thesis.

123 9. Exoskeleton de-adaptation is an important aspect of exoskeleton operation that should be further investigated, as cognitive factors were found to be related to individualized variation in exoskeleton de-adaptation.

10. A set of interrelated definitions for the terms gait characteristic, gait goal, gait strategy, and gait adaptation were proposed to provide a framework for quantitatively evaluating and operationally interpreting higher order gait constructs (i.e. strategy and adaptation) using specific observable features (i.e. gait characteristics) in the context of concrete gait goals and priorities.

7.3 Applications and Recommendations

The results of this thesis enable a set of recommendations pertaining to both exoskeleton system design and design of training methodologies for exoskeleton use. System and training design can lead to greater cognitive fit and, therefore, better human performance (i.e. gait task achievement or secondary task performance) with exoskeletons and related TCLE systems. System feedback can be provided using a variety of input modalities, but different input modalities have unique limitations and uses. Given the tightly-coupled nature of exoskeletons, tactile feedback is a natural candidate to provide the operator with information about the system, whether regarding what mode the system may be operating in, any mode changes that occur, or timely system warnings. However, this thesis showed in Chapter 2 that alternative interface modalities can impact task performance, in particular tactile input cues lead to decreased response rates and accuracy (Figure 2-3), or increased response times (Figure 2-2) as compared to visual cues. Thus, feedback design should be informed by how different modalities impact individuals’ system interaction. For example, when the response to a cue must happen very quickly, it is recommended that visual cues be implemented as response times to those are faster than tactile cues. However, in a scenario that requires the communication of non-urgent information in a minimally distracting way, tactile cues that do not require visual attention may be most appropriate given that requiring directed visual attention away from a primary goal, i.e. gait, can lead to decreased response accuracy (Figure 4-3). Feedback can be used to improve operational performance, especially when not walk-

124 ing preferred walking speeds (PWS). Chapter 4 showed that walking at non-PWS requires greater attentional resources, which can in turn lead to decrements in secondary task performance 4-3 and changes in gait speed achievement (Figure 4-5). When using exoskeletons in operational environments, simultaneous completion of secondary tasks is necessary, e.g. operating a mobile phone, holding a conversation with a companion, or maintaining visual attention and appropriately reacting to street traffic and signage. With one’s attention being directed to so many different multi-modal cues, system operation may take a backseat, leading to loss of awareness of what the system may be doing. System feedback provision can mitigate risks associated with divided attention, for example by directing individuals’ attention toward the exoskeleton when necessary, such as during a mode change that may impact their stability while walking.

This thesis has shown that individuals change their gait strategies uniquely in response to exoskeleton power addition or removal (Figures 5-3 and 5-4). There was further discussion of the different aspects of gait individuals may optimize for during exoskeleton operation, i.e. metabolic efficiency, coordination, stability, and gait repeatability (Section 6.2), and how differences in varying cognitive factors may indicate individuals are prioritizing one component of gait strategy over another. Knowing which components of gait strategies different individuals may be prioritizing can inform the design of explicit, targeted training protocols that instruct exoskeleton operators to utilize desired gait strategies. For example, individuals that seem more cautious (e.g. as measured by show slower reaction times) may be more likely to prioritize stability over metabolic efficiency and coordination with the exoskeleton. To encourage them to utilize the system appropriately, it may be beneficial to instruct them to increase their stride lengths after some time walking with a powered exoskeleton, thus enabling greater metabolic efficiency and coordination with the system. On the other hand, if an individual with a gait abnormality or compromised stability is utilizing an exoskeleton, e.g. a stroke patient, they should be instructed to maintain shorter stride lengths to enable stability and mitigate fall risk.

In general, it is recommended that exoskeleton training encourage a balance between different aspects of gait strategy, i.e. stability, coordination, metabolic efficiency, andgait repeatability. Different exoskeleton operation goals can inform which of these gait components are most desired and which can be de-prioritized if necessary. For example, exoskeleton use for stroke or spinal cord injury walking rehabilitation should initially prioritize stability

125 to ensure patient safety and mitigate fall risk. However, after some time and experience with the system, patients may be able to prioritize coordination at the expense of some stability. Young healthy adults aiming to use an exoskeleton system to run long distances or carry heavy objects, on the other hand, should prioritize coordination and metabolic efficiency from the beginning. Understanding of the specific goals of TCLE system usage and any requirement of simultaneous secondary task performance will be crucial to informing TCLE system design, training, and feedback.

7.4 Potential Future Work

A subset of questions relating to human interaction with exoskeletons and related tightly- coupled lower-extremity systems were answered in this thesis, with particular emphasis on aspects of individuals’ cognitive factors that may underlie TCLE system interaction. These questions sit at the intersection of a number of fields with many open questions. The following sections present potential future directions that would provide a more holistic picture of human-exoskeleton interaction and enable more informed exoskeleton (and other TCLE system) design and training, for the full diversity of the human population.

7.4.1 Additional Exoskeleton Gait Characteristics

There may be any number of intersecting and sometime conflicting priorities during gait with a TCLE system, a subset of which include metabolic efficiency, coordination with the system, gait repeatability, and stability. This thesis presented spatiotemporal gait characteristics during exoskeleton gait in Chapter 5 that elucidate only select aspects of these gait strategy components. In particular, those measures of NSL and NSW provided insight into individuals’ exoskeleton gait strategy regarding AP and ML stability. Measures of individuals’ speed achievement with the self-paced treadmill in Chapter 4 provided some knowledge of gait repeatability. Further knowledge of metabolic economy and system coordination can and should provide a more holistic view of individuals’ gait strategy with TCLE systems. While not measured in the work presented in this thesis, metabolic cost and muscle activation during exoskeleton use are commonly utilized metrics during exoskeleton operation. Metabolic cost reduction is often used to assess effectiveness of system design [164, 61, 122], as the goal of exoskeletons so often is to reduce metabolic costs. Muscle activation and

126 co-contraction patterns are used to provide information regarding individuals’ adaptation to exoskeleton power [183, 83, 65]. Related work in the Stirling group is currently exploring how muscle co-contraction patterns may be used as metrics of the operators fluency and coordination with the exoskeleton. However, as discussed in Chapters 5 and 6, changes in metabolic cost or coordination may intersect with changes in stability and gait repeatability. Thus, exploration of how cognitive factors presented in this thesis may relate to measures of individuals’ metabolic cost, muscle activation patterns, or coordination with a lower-extremity exoskeleton will provide a more complete picture of human-exoskeleton interaction and cognitive fit.

7.4.2 Perceptual Factors Underlying TCLE System Use

The goal of this thesis was to evaluate a number of cognitive factors that may underlie exoskeleton gait characteristics, but those are not the only factors endogenous to individuals that may impact how one interacts with machines like TCLE systems. The HESA study collected data surrounding individuals’ sensory and proprioceptive abilities as well. These perceptual factors likely also play a role in human-system interaction, given the requirement to perceive cues if one desires to comprehend and respond to those cues. Perceptual feedback can improve task performance in tele-operative systems [136] and TCLE systems by design can provide many tactile cues that must be understood by the operator. Additionally, systems like exoskeletons and self-paced treadmills require a strong understanding of one’s body position in space relative to the TCLE system. It is very possible that individuals with lower sensory or proprioceptive ability may be unable to properly utilize TCLE systems. Future work should explore relationships between baseline perceptual factors and exoskeleton gait characteristics in the same way this thesis approached baseline cognitive factors.

7.4.3 Gait Adaptation with TCLE Systems

This thesis focused on steady state exoskeleton gait characteristics. In particular, mean NSL and NSW, as well as changes in these means were evaluated to assess aspects of exoskeleton gait strategy. As defined in Chapter 1, adaptation can be considered temporal changes in gait strategy. Exoskeleton adaptation work provides important information about human- exoskeleton interaction, though this literature focuses solely on physiological changes in parameters such as muscle activation patterns and joint angle profiles [65, 90]. Such work

127 does not explore the cognitive factors that may underlie these transient changes in exoskeleton gait. Though this thesis explored differences in exoskeleton gait characteristics at different points in time after exoskeleton power was added or removed, these datawere presented as means across large time buckets, i.e. during two minutes of exoskeleton walking. Understanding changes in exoskeleton gait characteristics on shorter timescales, such as on the order of seconds, can provide knowledge about what individuals may adapt very quickly, or those that do not shift gait characteristics immediately, but may show a delay in adaptation. Individuals in the HESA study qualitatively showed visible adjustments in gait when exoskeleton power was first added, with some even looking as though they may trip (though no participants tripped or fell during exoskeleton use). Such changes over very short timescales could provide important information about exoskeleton design. For example, exoskeleton power addition at specific portions of the gait cycle may result in differential gait characteristics or adaptation for individuals with specific cognitive or perceptual factors. Thus transient changes in exoskeleton gait characteristics and any relationships with baseline cognitive or perceptual factors should be further explored.

7.4.4 Longitudinal Use of TCLE Systems

Just as transience in changes to exoskeleton gait strategy are important, so are longitudinal changes. Operational use of exoskeletons will require long term utilization far beyond the 20 minutes of exposure provided in this thesis and in much of the current exoskeleton literature. This thesis showed that some measures of gait may reach a consistent state after short periods of time with a TCLE system, such as mean metrics of speed achievement on the self-paced treadmill in Chapter 4. However, other components of strategy continue to change in subtle ways over longer timescales. Shifts in exoskeleton gait strategy have been seen when individuals are exposed to the same ankle exoskeleton across numerous days [65, 90]. Similar shifts were observed in this thesis. Individuals were more quickly able to achieve slower and faster speeds during their second bout of walking on the SPT as compared to their first bout, even with the addition of a distracting secondary task (Figure 4-5d). Cognitive factors may indicate how individuals will shift their gait strategies over longer periods of time walking with an exoskeleton. Chapter 6 showed select relationships between probes of reaction time and attention with gait characteristics changes during de-adaptation and after five minutes of de-adaptation relative to individuals’ exoskeleton baselines walking.

128 These relationships should be further explored to assess how individuals may shift their gait after longer periods of exposure to exoskeletons. It may be that individuals begin to learn and store new gait strategies when walking with an exoskeleton, and these gait patterns may last long after the system is turned off.

7.4.5 Creation of a Cognitive Fit Checklist

Good cognitive fit between a system and its user is the idea that the system supports the perception-cognition-action pathways of the user [185]. In the context of tightly-coupled systems like exoskeletons, this requires good physical fit at a minimum. However, the system must also enable the user to have a good understanding of the functioning and goals of the system. For example, if the system is changing modes, e.g. changing the assistance torque profile, the operator should understand that so they can react appropriately, e.g. by increasing stride lengths. As shown in Chapter 6, individuals with faster reaction times to tactile cues may be more likely to respond to exoskeleton assistance by increasing their stride lengths. The preliminary correlations found in Chapter 6 should be used to inform the creation of a checklist of cognitive factors that may inform cognitive fit between humans and systems like exoskeletons. It may be that individuals with cognitive factors like faster reaction times and greater ability to complete secondary tasks under divided attention may have a better cognitive fit with exoskeletons and similar systems. Additionally, further research exploring feedback and training methodologies informed by the relationships found in Chapter 6 (e.g. assessment of how tactile feedback impacts gait characteristics, perhaps by improving an individuals’ understanding of system operation in real time) can enable the design of systems that provide better cognitive fit for individuals with a greater variety of baseline cognitive factors.

7.4.6 Extension to Patient Populations

This thesis explored human-system interaction with young, healthy individuals, providing a foundation from which to begin understanding TCLE system operation. However, exoskeletons and TCLE systems have great potential to aid in rehabilitation and functional augmentation for individuals with gait pathologies. Treadmills are already commonly utilized to assist with rehabilitation and gait training for stroke and spinal cord injury (SCI) patients [108, 155]. While the effectiveness of exoskeletons in similar contexts is not well

129 established, there are calls for further investigation of exoskeletons as rehabilitative and augmentative devices [94, 57], and limited evidence exists that exoskeleton-augmented rehabilitation can improve walking in spinal cord injury patients [53]. This literature should be extended.

Exoskeletons provide great potential improving gait and independent functionality of individuals with gait pathologies, but there is not yet enough evidence of true benefits. There is also a dearth understanding regarding what factors (i.e. cognitive, perceptual, or pathology-based) may be indicative of whether an individual would benefit from exoskeleton operation, either in a rehabilitation or operational setting. For example, there is limited data on how different injury types may affect exoskeleton gait characteristics and ability to operate these systems, and the extant data does not provide a clear picture on how injury type impacts exoskeleton usage [189]. This lack of clarity makes the determination of clinical indications for exoskeleton use very difficult to define. Future work should explore how individuals with varying types of gait pathologies, e.g. stroke, SCI, Parkinson’s’ Disease, etc., operate exoskeletons, and what cognitive factors, in particular when cognition is impacted by pathology, may underlie and impact their exoskeleton gait characteristics.

7.5 Concluding Remarks

This thesis begins to provide insights into the broad question we first began with: why do some people easily and quickly adapt to walking with exoskeletons, while others do not? This is a complex, multi-faceted question that must be approached from a variety of perspectives. This thesis approached the question from a human factors lens, aiming to understand how the physical and cognitive interactions users have with exoskeletons lead to differential movement and task performance outcomes. The unique design of exoskeletons as wearable systems that are tightly-coupled to the body of the operator provide novel avenues of human-system interaction. This thesis has shown that these new types of interactions impact how humans walk with these systems, as well as how they interact with the world around them during system operation. Different baseline cognitive factors (e.g. faster reaction times) may lead individuals to prioritize different components of gait when walking with exoskeletons, whether that be coordination with the system or gait stability. Each of these gait components must be appropriately balanced in varying exoskeleton use cases.

130 Knowledge of what cognitive factors underlie individualized exoskeleton gait strategies empowers exoskeleton designers and trainers to create systems that are safe and effective for all different kinds of individuals. Young healthy individuals can have very different gaitand cognitive characteristics than older adults. Disabled individuals of any age may have gait pathologies or other cognitive differences that impact their use of exoskeletons. The work presented in this thesis provides foundational knowledge of how varying cognitive factors and ability impact human-system interaction. This foundation should be expanded upon to ensure that there is equitable access to exoskeletons and similar TCLE systems for all individuals, regardless of health status or ability.

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148 Appendix A

Supplementary Figures and Tables

A.1 Generalized Linear Mixed Effects Model for SP & SPDT Data

Name Est. SE t-stat p-val Lower Upper 1 (Intercept) 9.024 0.014 647.118 <0.001 8.997 9.051 2 Trial2 -0.049 0.003 -16.053 <0.001 -0.055 -0.043 3 Speed1 -0.001 0.013 -0.073 0.942 -0.027 0.025 4 Speed1.3 -0.018 0.013 -1.341 0.180 -0.044 0.008 5 Speed1.5 -0.106 0.013 -7.960 <0.001 -0.133 -0.080 6 Trial2:Speed1 0.007 0.004 1.530 0.126 -0.002 0.015 7 Trial2:Speed1.3 0.034 0.004 8.802 <0.001 0.026 0.041 8 Trial2:Speed1.5 0.066 0.004 14.823 <0.001 0.057 0.074 9 DVtype2 0.215 0.003 80.063 <0.001 0.210 0.221 10 Trial2:DVtype2 0.090 0.004 22.073 <0.001 0.082 0.097 11 Speed1:DVtype2 -0.018 0.004 -4.701 <0.001 -0.025 -0.010 12 Speed1.3:DVtype2 -0.015 0.003 -4.348 <0.001 -0.022 -0.008 13 Speed1.5:DVtype2 0.066 0.004 17.138 <0.001 0.059 0.074 14 Trial2:Speed1:DVtype2 -0.040 0.006 -6.906 <0.001 -0.051 -0.028 15 Trial2:Speed1.3:DVtype2 -0.078 0.005 -15.353 <0.001 -0.088 -0.068 16 Trial2:Speed1.5:DVtype2 -0.107 0.006 -18.406 <0.001 -0.119 -0.096 17 DVtype3 -2.015 0.006 -345.156 <0.001 -2.026 -2.003 18 Trial2:DVtype3 0.154 0.009 17.977 <0.001 0.137 0.171 19 Speed1:DVtype3 -0.545 0.009 -57.417 <0.001 -0.563 -0.526

149 Name Est. SE t-stat p-val Lower Upper 20 Speed1.3:DVtype3 -0.717 0.009 -80.859 <0.001 -0.734 -0.700 21 Speed1.5:DVtype3 -0.612 0.010 -61.162 <0.001 -0.632 -0.592 22 Trial2:Speed1:DVtype3 -0.151 0.014 -10.661 <0.001 -0.179 -0.124 23 Trial2:Speed1.3:DVtype3 -0.184 0.013 -14.501 <0.001 -0.209 -0.159 24 Trial2:Speed1.5:DVtype3 -0.320 0.015 -20.882 <0.001 -0.350 -0.290 25 DVtype4 0.442 0.003 172.116 <0.001 0.437 0.447 26 Trial2:DVtype4 -0.123 0.004 -30.899 <0.001 -0.130 -0.115 27 Speed1:DVtype4 -0.377 0.004 -99.391 <0.001 -0.384 -0.369 28 Speed1.3:DVtype4 -0.326 0.003 -94.750 <0.001 -0.333 -0.319 29 Speed1.5:DVtype4 0.035 0.004 9.447 <0.001 0.028 0.042 30 Trial2:Speed1:DVtype4 0.198 0.006 34.241 <0.001 0.186 0.209 31 Trial2:Speed1.3:DVtype4 0.188 0.005 37.016 <0.001 0.178 0.198 32 Trial2:Speed1.5:DVtype4 -0.064 0.006 -11.232 <0.001 -0.075 -0.053 33 DVtype5 -0.005 0.003 -1.647 0.100 -0.010 0.001 34 Trial2:DVtype5 0.077 0.004 17.964 <0.001 0.068 0.085 35 Speed1:DVtype5 0.415 0.004 108.273 <0.001 0.408 0.423 36 Speed1.3:DVtype5 0.592 0.004 169.098 <0.001 0.586 0.599 37 Speed1.5:DVtype5 0.753 0.004 197.071 <0.001 0.745 0.760 38 Trial2:Speed1:DVtype5 -0.021 0.006 -3.646 <0.001 -0.032 -0.010 39 Trial2:Speed1.3:DVtype5 -0.070 0.005 -13.504 <0.001 -0.080 -0.059 40 Trial2:Speed1.5:DVtype5 -0.080 0.006 -13.998 <0.001 -0.092 -0.069 41 DVtype6 -1.022 0.004 -262.642 <0.001 -1.030 -1.015 42 Trial2:DVtype6 0.024 0.006 4.025 <0.001 0.012 0.035 43 Speed1:DVtype6 -0.022 0.006 -4.050 <0.001 -0.033 -0.012 44 Speed1.3:DVtype6 -0.025 0.005 -4.966 <0.001 -0.035 -0.015 45 Speed1.5:DVtype6 0.079 0.006 14.259 <0.001 0.069 0.090 46 Trial2:Speed1:DVtype6 -0.042 0.008 -4.992 <0.001 -0.058 -0.025 47 Trial2:Speed1.3:DVtype6 -0.059 0.007 -7.862 <0.001 -0.073 -0.044 48 Trial2:Speed1.5:DVtype6 -0.112 0.008 -13.237 <0.001 -0.129 -0.096 49 DVtype7 0.628 0.002 253.329 <0.001 0.623 0.633 50 Trial2:DVtype7 0.042 0.004 11.063 <0.001 0.034 0.049 51 Speed1:DVtype7 -0.285 0.004 -79.036 <0.001 -0.292 -0.278 52 Speed1.3:DVtype7 -0.383 0.003 -115.372 <0.001 -0.389 -0.376 53 Speed1.5:DVtype7 -0.352 0.004 -94.187 <0.001 -0.360 -0.345 54 Trial2:Speed1:DVtype7 -0.016 0.005 -2.892 0.004 -0.027 -0.005

150 Name Est. SE t-stat p-val Lower Upper 55 Trial2:Speed1.3:DVtype7 -0.012 0.005 -2.550 0.011 -0.022 -0.003 56 Trial2:Speed1.5:DVtype7 -0.036 0.006 -6.470 <0.001 -0.047 -0.025

Table A.1: Coefficients estimated by a GLME incorporating seven DVs with categorical predictors Trial (1 - SP, 2 - SPDT), Speed (0.5, 1.0, 1.3, 1.5), DVtype (1 - GTP, 2 - ratio of mean measured to target speed, 3 - measured speed COV, 4 - NRT, 5 - NSL, 6 - NSW, 7 - ST), and random factor Subject. DF = 1725 for all estimates. Coefficients for each DV are grouped together.

151 A.2 Initial Exoskeleton Walking Box Plots

Figure A-1: Normalized stride width box plots during baseline (un-powered exoskeleton before the exoskeleton was powered on) at four different speeds for subjects EXO103 to EXO110. B1: 0.5 m/s, B2: 1.0 m/s, B3: 1.3 m/s, B4: 1.5 m/s. Significant pairwise differences are marked with an asterisk below boxes. Asterisks above a box indicate that box is significantly different than all other boxes for the same subject.

152 Figure A-2: Normalized stride width box plots during baseline (un-powered exoskeleton before the exoskeleton was powered on) at four different speeds for subjects EXO111 to EXO118. B1: 0.5 m/s, B2: 1.0 m/s, B3: 1.3 m/s, B4: 1.5 m/s. Significant pairwise differences are marked with an asterisk below boxes. Asterisks above a box indicate that box is significantly different than all other boxes for the same subject.

153 Figure A-3: Normalized stride length box plots during all exoskeleton walking conditions at 1.3 m/s for subjects EXO103 to EXO108. Inset shows timing of the six walking conditions during the full exoskeleton walking protocol. Significant pairwise differences are not shown.

154 Figure A-4: Normalized stride length box plots during all exoskeleton walking conditions at 1.3 m/s for subjects EXO109 to EXO113. Inset shows timing of the six walking conditions during the full exoskeleton walking protocol. Significant pairwise differences are not shown.

155 Figure A-5: Normalized stride length box plots during all exoskeleton walking conditions at 1.3 m/s for subjects EXO114 to EXO118. Inset shows timing of the six walking conditions during the full exoskeleton walking protocol. Significant pairwise differences are not shown.

156 Figure A-6: Normalized stride width box plots during all exoskeleton walking conditions at 1.3 m/s for subjects EXO103 to EXO108. Inset shows timing of the six walking conditions during the full exoskeleton walking protocol. Significant pairwise differences are not shown.

157 Figure A-7: Normalized stride width box plots during all exoskeleton walking conditions at 1.3 m/s for subjects EXO109 to EXO113. Inset shows timing of the six walking conditions during the full exoskeleton walking protocol. Significant pairwise differences are not shown.

158 Figure A-8: Normalized stride width box plots during all exoskeleton walking conditions at 1.3 m/s for subjects EXO114 to EXO118. Inset shows timing of the six walking conditions during the full exoskeleton walking protocol. Significant pairwise differences are not shown.

159 A.3 Baseline and Exoskeleton Parameter Correlation Plots

160 Figure A-9: Exoskeleton gait and baseline comparisons 1-6 listed in order of presentation in Table 6.3.

(a) ΔNSLB3A1 vs. RTcon (b) ΔNSLB3A1 vs. 휇(GTP)all

(e) ΔNSLA3D1 vs. 휇(Bias)all (f) ΔNSLA1A3 vs. ΔRT

161 Figure A-10: Exoskeleton gait and baseline comparisons 7-12 listed in order of presentation in Table 6.3.

(a) ΔNSLA1A3 vs. DTD(Bias)0.5 (b) ΔNSLA1A3 vs. DTD(Bias)1.3

(e) ΔNSLD1D2 vs. DTD(NSW)all (f) ΔNSLD1D2 vs. 휇(GTP)all

162 Figure A-11: Exoskeleton gait and baseline comparisons 13-18 listed in order of presentation in Table 6.3.

(a) ΔNSLD1D2 vs. 휇(GTP)1.3 (b) ΔNSLD1D2 vs. DTD(Bias)all

(e) ΔNSLB3D2 vs. 휇(GTP)all (f) ΔNSLB3D2 vs. 휇(GTP)1.3

163 Figure A-12: Exoskeleton gait and baseline comparisons 19-24 listed in order of presentation in Table 6.3.

(a) ΔNSWB3A1 vs. RTcon (b) ΔNSWD1D2 vs. ΔRT

(e) ΔNSWB3D2 vs. RTincon (f) ΔNSLB3A1 vs. 휇(Bias)0.5

164 Figure A-13: Exoskeleton gait and baseline comparisons 25-30 listed in order of presentation in Table 6.3.

(a) ΔNSLB3A1 vs. 휇(GTP)0.5 (b) ΔNSLB3A1 vs. 휇(NSW)0.5

(e) ΔNSLB3D2 vs. 휇(Bias)0.5 (f) ΔNSLB3D2 vs. 휇(GTP)0.5

165 Figure A-14: Exoskeleton gait and baseline comparisons 31-33 listed in order of presentation in Table 6.3.

(a) ΔNSWD1D2 vs. 휇(GTP)1.5 (b) ΔNSWB3D2 vs. 휇(Bias)0.5

166 Appendix B

HESA Study Task Protocols

Protocols for the sensory and motor tasks implemented in the HESA study are presented here. Data from these tasks were not analysed in this thesis but further analysis can provide a more complete picture of sensorimotor factors that may underlie individualized exoskeleton gait strategy. This appendix provides details on the sensory, motor, and exoskeleton walking protocols implemented in the HESA study that were not addressed and discussed in this thesis. Infor- mation on the demographic and cognitive surveys collected during the study are presented in Appendix C.

B.1 Perceptual Tests

Two perceptual measures were collected in the HESA study: sensory thresholds and proprioceptive ability. Two unique tests were included to enable measurement of these two perceptual factors. First, to assess individuals’ sensory thresholds, the Semmes-Weinstein monofilament test was selected [31] to provide information on how well subjects areable to discriminate touch. Physical interactions between TCLE systems and an operator could affect their perception of what the system is doing and therefore their interaction withthe system. Measure of proprioceptive ability at the ankle provides information on how well subjects can sense and control their own movements. Greater ability to sense and replicate limb positions may indicate greater overall proprioceptive ability which may be related to how individuals select gait strategies with an exoskeleton. An ankle angle replication protocol was adapted from tests found in the literature to assess joint proprioception [70, 52]. Both

167 tests were completed with and without an exoskeleton to evaluate how sensory thresholds and proprioceptive ability was impacted by donning an ankle exoskeleton.

B.1.1 Semmes-Weinstein Monofilament Test

The test protocol was completed only on dominant leg. Each filament size (in grams: 2.0, 1.4, 1.0, 0.6, 0.4, 0.16, 0.07, 0.04, 0.02, 0.008) was tested in decreasing weight order at each location until 3 missed responses. Specific test protocol:

1. Mark each test location with a surgical marker, do not vary test location by more than 1 cm.

2. Instruct subject to verbally indicate when they sense a filament.

3. Start with largest filament, hold perpendicular to skin surface, press down until the fiber is deflected about 1cm. Hold for 1 second. Be careful not to disturb hairinthe area; ensure the filament does not slide against the subjects’ skin.

4. Allow subject to respond if they feel the filament. Vary timing between touches so subject does not notice a timing pattern.

5. Repeat with same size filament up to 5 times. Subjects should sense at least 3ofthe 5 trials to count as a success. Three or more trials in which subject does not sense the filament counts as a miss, threshold should be recorded as filament size directly larger than the missed filament.

6. Decrease size consecutively until a missed response.

B.1.2 Ankle Angle Replication Test

1. Place subject’s foot on rotating disc placed at the center of a protractor with big toe pointed at 90 degrees (Figure B-2).

2. Ask subject to internally rotate as far as possible without any part of their foot lifting off the rotating disc. Mark that angle as 100% internal rotation on the data collection sheet to populate internal rotation target angles.

168 Figure B-1: Testing locations on dominant leg for Semmes-Weinstein monofilament test.

169 Figure B-2: Birds eye view schematic of how the AAR test was implemented. All subjects were tested with their right foot; turning the ankle inward (toward 0o) constituted an internal rotation, turning the ankle outward (toward 180o constituted an external rotation.

3. Ask subject to externally rotate as far as possible without any part of their foot lifting off the rotating disc. Mark that angle as 100% external rotation on the data collection sheet to populate external rotation target angles.

4. With the subjects’ eyes closed, move ankle around randomly while holding knee sta- tionary, then place their foot at the target angle.

5. Tell subject to remember this position and hold it for a second. Let go of their foot and leave there for 1 second. If foot drifts away from target angle, note down the new angle the foot comes to rest at.

6. Move subjects’ foot randomly back and forth, then place foot in starting position, with toe pointed to 0. Ask subject to recreate previous position. Tell them to verbally indicate when they believe they have reached the target angle.

7. Note ending angle when subject indicates that they have reached the target position.

8. Repeat until all target positions have been tested.

170 Table B.1: Table of all AAR trial internal/external rotation proportions. Actual angles must be calculated from measured maximal internal and external rotation. Proportion of maximal internal or external rotation are noted in this table (i.e. for trial 1, External 0.25, if maximal external rotation was 40 degrees, actual target angle would be External - 10 degrees).

All AAR trials External 0.25 Internal 0.5 Zero 0 Internal 0.8 External 0.8 External 0.1 External 1 Internal 0.25 Internal 1 Internal 0.1 Internal 0.5 External 0.5 Internal 1 External 0.1 External 1 zero 0 Internal 0.1 External 0.25 External 0.8 Zero 0 Internal 0.8 Internal 0.25 External 0.5

B.2 Motor Tasks

The HESA study included two motor tasks. First, a static balance task was completed with and without a worn exoskeleton to provide a baseline measure of individuals’ medio-lateral and antero-posterior balance while free standing and to assess any changes in static balance caused by the donning of an ankle exoskeleton. Sway is a predictor of fall risk and provides quantification of an individual’s balance and coordination ability [88]. Both are important factors when considering gait and may be predictive of individuals’ gait strategy characteristics during exoskeleton use. Second, a split-belt motor adaptation task was completed to assess individuals’ ability to adapt to motor perturbations. Split-belt walking has been commonly used in literature to measure and understand various aspect of motor adapta-

171 tion, including time to steady state, effect of feedback on motor strategy, and transfer ofgait characteristics to different scenarios (i.e. to over ground walking) [123, 159, 112, 103, 194]. Testing split-belt walking provided a baseline motor adaptation measure for individual subjects that is based on well-established literature, allowing for a more direct comparison of motor adaptation in the present study to data to the motor adaptation literature.

B.2.1 Static Balance

Subjects completed each of six static balance trials (three each with eyes open and closed, on each leg individually and on both legs) in the same order (Table B.2. Subjects were given a 15 second break after each 30 second trial if they desire. This trial order was repeated three times. Subjects were instructed to hold their foot behind them and place their knee at a 90-degree angle with their shin parallel to the floor during single stance trials. They were also instructed to hold their hands at their sides. The instruction script provided was:

We will be measuring your balance in this test. You will on both feet with your eyes open, then both feet with your eyes closed, then on each foot, with eyes open. Whenever you are standing on one leg, hold your other leg such that your foot is behind your body and your knee is at a right angle. You’ll do each trial 3 times with a 15 second break between each trial during which you can reset and relax. We will let you know when to start and when you can stop. When you are standing on both feet, keep your feet shoulder distance apart with your toes pointing forward and look straight ahead. Keep your hands at your sides during double and single stance trials.

Table B.2: All static balance trials

Stance Eyes (O/C) Time (sec) Double Open 30 Double Closed 30 Left Open 30 Right Open 30 Left Closed 30 Right Closed 30

172 B.2.2 Split-Belt Adaptation

Individuals walked for a total of 16 minutes on a split-belt treadmill, during which eight minutes were with tied belt speeds and eight minutes with different belt speeds (Table B.3). Individuals were not given any timely warning of when belt speed changed, but they were provided with an overview of the entire protocol prior to beginning the trial. Participants were given the following instruction script:

For the next 16 minutes you will be walking on the treadmill. The treadmill will move at different speeds. At some point, the speed of the two treadmill belts will be different. Try to maintain as natural a gait as possible during all times.You will not get a warning before the treadmill belts change speed or begin to move at different speeds. It can feel like a trip, so be prepared to catch your balance during these transition points. Feel free to look down at the treadmill as you’re walking, it’s important to not cross your feet over to the opposite treadmill belt, as that could trip you.

Table B.3: Overview of split-belt walking trial

Duration (min) Right belt speed (m/s) Left belt speed (m/s) 2 0.7 0.7 2 1.4 1.4 8 1.4 0.7 4 0.7 0.7

B.3 Varied Speed Exoskeleton Walking

Participants completed four exoskeleton walking protocols in total during the HESA study. Data from only the first bout of exoskeleton use (Day 1 Initial Exoskeleton Walking) was presented in this thesis. However, there exists data for exoskeleton-augmented walking across two days and with two different protocols. Individuals repeated the Initial Exoskeleton Walking protocol on Day 2, then completed two bouts of Varied Speed Exoskeleton Walking, VSW (Figure B-3). Participants were given the following instructions prior to completing the VSW protocol:

You will be walking at varying speeds with the exoskeleton. The exo will be powered off for the first 4 minutes of walking, during which the treadmill will

173 Figure B-3: Varied speed exoskeleton walking (VSW) protocol details, completed by HESA study participants twice on Day 2. Individuals walked with the system for just under 25 minutes. The first four minutes of the VSW were identical to IEW, with one minuteeach at four different speeds with the system powered off. Then the exoskeleton was poweredon and individuals walked for five minutes at each of three walking speeds - 1.0, 1.3, and1.5 m/s. Individuals did not walk with the system powered on at 0.5 m/s because the system did not actuate and provide assistance when walking at that speed. Individuals were then given five minutes to de-adapt at 1.3 m/s, as in the IEW protocol.

move at four different speeds: 0.5, 1.0, 1.3 and 1.5 m/s. Then the exowillbe powered on, running control policy 1. You will walk with the exo powered on for 20 minutes, 5 minutes at each of the four speeds mentioned. Then you will do a repeat of the first block in which you will move at the four speeds infour minutes. After this, the control policy on the exo will change, so you may feel some changes in how the system is assisting you. You will walk for another 20 minutes at the four different speeds with this new exo control policy. Finally, you’ll do another repeat of the four minutes at four speeds.

174 Appendix C

Study Questionnaires

Note: all surveys were administered on the Qualtrics survey platform (Qualtrics, Provo, UT), an online survey tool. Question wording and answer choices (in the case of multiple choice responses) are presented in this appendix.

C.1 Pre-experiment Questionnaire

1. Subject Number

2. Age

3. Height

4. Weight

5. Shirt size (XS, S, M, L, XL)

6. How many hours of sleep did you get last night?

7. Is last night’s amount of sleep above average, average, below average as compared to the amount of sleep you usually get?

∙ Above average

∙ Average

∙ Below average

8. How much and what kind of caffeine have you had today? (ie. 2 cups of coffee, 1can of soda, 3 cups of tea)

175 9. Is your caffeine intake today above average, average, or below average as compared to usual?

∙ Above average

∙ Average

∙ Below average

10. How energetic do you feel today, on a scale of 1 to 10 (1 being no energy and 10 being the most energetic you have ever felt)?

11. Have you had any alcoholic drinks in the past 24 hours? (Y/N)

12. If (11=YES): How many drinks did you have?

13. Which hand do you write with?

∙ Left

∙ Right

∙ Other

14. Which foot do you kick with?

∙ Left

∙ Right

∙ Other

15. Have you have any leg or foot injuries in the last 10 years? (Y/N)

16. If (15=YES): Please describe the injury (location, side (left/right), and how long ago).

17. What types of footwear do you use on a regular or semi-regular basis? (ie. tennis shoes, various types of high heels, ski boots, flip flops, etc.)

18. How often do you exercise and what does your exercise routine consist of? (i.e. running 3hrs/week, weightlifting 1hr/day, swimming 2hrs/week, etc.)

19. How often do you play video games currently (hours/week)?

20. How often did you play video games in the past? (hours/week)

176 21. What types of video games do you/did you play? (i.e. first-person shooter, racing, role play, etc.) If you never played video games, please write N/A.

22. Do you use any foot pedal controllers in your games? (Y/N)

23. If you played competitive sports in high school, what did you play and for how many years? (i.e. soccer, 3yrs) If you did not play competitive sports in high school, please write N/A.

24. If you played competitive sports in college, what did you play and for how many years? If you did not play competitive sports in college, please write N/A.

25. Have you had any military experience? (Y/N) If Y, show questions 26-29. If N, go to question 30.

26. What was your rank (e.g. E-3, WO-4, O-2)?

27. What was your MOS (Primary, e.g. 11B)?

28. Describe your role (e.g. infantry).

29. What is the number of combat tours you have completed?

30. Have you ever worn a lower-body exoskeleton before?

31. IF (30=YES): What type of exoskeleton(s) did you wear and what was the total time you spent in it? (i.e. knee exo, 30 min; soft ankle exo, 1hr)

32. Have you experienced virtual reality before?

33. IF (32=YES): What type of VR environment did you use and what was the total time you spent in it? (i.e. Oculus, 30 min; immersive dome, 1hr)

34. Have you experienced augmented reality before?

35. IF (34=YES): What type of AR environment did you use and what was the total time you spent in it? (i.e. HoloLens, 30 min)

36. Do you have experience flying RC aircraft?

37. IF (36=YES): How much time in hours have you spent flying RC aircraft?

177 38. Do you hold or are currently working on acquiring a pilot’s license? If Y, show questions 39-40. If N, go to question 41.

39. How many hours of flight time do you have?

40. What is your rating?

41. When you use electronic devices (e.g. phones, tablets, etc.), how do you interface with them (e.g. touchscreen, keypad, video game controller, etc.)?

42. Is there any other relevant experience you would like to share?

C.2 Cognitive Surveys

C.2.1 Reinvestment Propensity Survey

Motor task performance can be hampered by directing attention consciously towards directing body movement. The movement-specific reinvestment scale measures propensity of individuals to consciously control movement. Individuals’ reinvestment propensity may correlate to their adaptability to exoskeleton use.

1. I am always trying to think about my movements when I carry them out.

2. I reflect about my movements a lot.

3. I am always trying to figure out why my actions failed.

4. I am aware of the way my body works when I am carrying out a movement.

5. I rarely forget the times when my movements have failed me.

6. I am concerned about my style of moving.

7. I am self-conscious about the way I look when I am moving.

8. If I see my reflection in a shop window, I will examine my movements.

9. I sometimes have the feeling that I am watching myself move.

10. I am concerned about what people think about me when I am moving.

178 C.2.2 Technology Adoption Survey

This survey consists of 7 questions related to subjects’ thoughts and attitudes towards technology. These questions are presented to subjects twice with two different prompts. First, subjects are asked to consider technologies such as smartphones/tablets when answering the following questions. Immediately following this, subjects are asked to answer the same set of questions when considering technologies such as self-driving cars; thus, subjects answer 14 questions in total as a part of this survey. Questions are phrased as statements and subjects are asked to rate their level of agreement or disagreement with the phrase on a Likert scale. The following questions are presented to subjects:

1. I believe that most technologies are effective at what they are designed todo.

2. I usually trust a technology until it gives me a reason not to trust it.

3. A large majority of technologies are excellent.

4. It is easy to learn to use most technologies.

5. Using most technologies requires extra focus and attention.

6. Most technologies are safe to use.

7. Most technologies behave as expected during use.

C.2.3 Dephy Exoskeleton Expectations Survey

This survey consists of 17 questions related to subjects’ expectations of the functionalities and effectiveness of the Dephy exoskeleton. Prior to this survey, subjects are giventhe following information:

“The Dephy exoskeleton is a system that assists you in walking. It does this by providing extra power to you at toe-off, or the point at which your foot is about to push off the ground. It uses on-board sensors to detect the position ofyour ankle and uses that information to time its assistance.”

Questions are phrased as statements and subjects are asked to rate their level of agreement or disagreement with the phrase on a Likert scale. The following questions are presented to subjects:

179 1. The exoskeleton will make it easier to walk over even terrain.

2. The exoskeleton will make it easier to walk over uneven/rugged terrain.

3. The exoskeleton will make it easier to step over obstacles.

4. The exoskeleton will make it easier to walk on inclines/step up on obstacles.

5. The exoskeleton will make it easier to walk on declines/step down off obstacles.

6. The exoskeleton will help me feel less tired following prolonged load carriage.

7. The exoskeleton will help me feel less sore following prolonged load carriage.

8. The exoskeleton will be easy to don/doff.

9. It will be easy to learn to use the exoskeleton.

10. I will quickly become comfortable using the exoskeleton.

11. Using the exoskeleton will require extra focus and attention.

12. The exoskeleton is safe to use.

13. The exoskeleton will perform its job without breaking.

14. The exoskeleton will behave as expected during use.

15. The exoskeleton will allow me to move normally/naturally.

16. I could see myself using the exoskeleton during a military operation.

17. I like the concept behind the exoskeleton.

C.2.4 Perceived Fluency Survey

This survey consists of 12 questions that attempt to measure participants’ subjective fluency. This survey is given to subjects after they complete the following tests: self-pacing; self-paced dual tasking; initial exoskeleton walking; and varied speed exoskeleton walking. The survey asks questions related subjects’ perceived trust, comfort, safety, and task performance while using the devices relevant to each test (the self-paced treadmill in the former two tests and the Dephy exoskeleton in the latter two tests). Questions are phrased as statements and

180 subjects are asked to rate their level of agreement or disagreement with the phrase on a Likert scale. The following questions are presented to subjects:

1. The system impeded me from completing the task.

2. I was comfortable using the system.

3. The system’s actions were predictable.

4. I felt stressed when something about the system changed.

5. I was able to walk smoothly with the system.

6. The system acted in a way I expected it to.

7. I was comfortable with my level of awareness about what they system was doing.

8. I felt safe using the system.

9. The system’s actions were consistent.

10. The system understood my intentions.

11. I felt as though I was fighting the system.

12. The system was dependable.

181 182 List of Figures

1-1 When utilizing a TCLE system, movement of either the human or the system necessarily invokes movement of the other. For example, in the context of wearable robotic device like an exoskeleton, if the operator takes a step, the exoskeleton must move as it is positioned on the operator’s leg, resulting in an interaction at the back of the person’s leg as the leg pushes the exoskeleton back. If the exoskeleton actuates first and moves backward, the operator’s leg must move in tandem with that actuation because the leg is encased within the system. This scenario results in a physical interaction at the front of the person’s leg because, in this case, the exoskeleton is pushing the user’s leg back. 15

1-2 Theoretical schematic of perception-cognition-action pathways relevant to TCLE system use and necessary for good cognitive fit. System users must be able to sense the cues of the system on their lower extremities (perception), comprehend those cues and integrate those with environmental information to make action decisions (cognition), and carry out those decisions (action). . 19

1-3 Example of the four different cues presented during a visual Simon task. The required response is the direction the arrow points. Congruent cues present the arrow spatially on the side of the directed response, while incongruent cues present the arrow in the spatially incongruous position...... 21

183 1-4 Schematic describing the relationship between gait strategy and adaptation during theoretical walking with an exoskeleton. The blue line shows changes in a gait characteristic over time. A weighted combination of this gait characteristic and others comprises a gait strategy. The change in this characteristic, and gait strategy overall, over time is termed adaptation. This change can be conscious or subconscious. For example, at A when the exoskeleton is turned on, the gait characteristic may change subconsciously, whereas the individual makes a conscious decision to modulate their gait near B...... 23

1-5 An example of a potentially metabolically costly gait strategy while using a powered ankle exoskeleton that provides added push-off power. This added powered has the result of propelling the foot forward, but individuals may fight that added power by activating muscles required to place their footon the ground sooner than necessary or optimal...... 27

2-1 A. Four treatment conditions were each unique combination of cue-response pair: visual cue/upper extremity response (abbreviated as VU), visual cue/lower extremity response (VL), tactile cue/upper extremity response (TU), and tactile cue/lower extremity response (TL). B. Congruent and incongruent visual cues were presented as arrows on the screen in front of participants on either the left or right side of the fixation point (crosshair at the center of the screen). Baseline (no-go) cues were presented as two horizontal bars in the place of the arrows. C. Tactile cues were presented using vibrotactile motors placed around participants’ thighs. Vibration locations were mapped to left/right congruent/incongruent cues as shown. Baseline (no-go) cues were defined as all four motor vibrating at the same time. Pictured as though participants are facing away...... 36

2-2 Response times for each cue-response pair split by congruency. RT were significantly different (p < 0.05) between each cue-response pair when pooled for congruency (differences not notated symbolically in figure). RT were also significantly faster (p < 0.05) for congruent stimuli than incongruent stimuli within each cue-response pair (notated with an asterisk)...... 39

184 2-3 Median accuracy and response rate (RR) across all subjects by cue-response pair. Asterisks denote significantly different pairwise comparisons at an alpha level of p < 0.05. Statistical values for Wilcoxon sign-rank tests and effect sizes can be found in table 2...... 41

3-1 Full two-day testing protocol ...... 49

3-2 The Dephy ankle exoskeleton consists of a carbon fiber footplate integrated into a standard mid-ankle boot or, in the case of the men’s size 6, a low- ankle shoe (seen here). The boot-footplate assembly is connected to a shank assembly on which a unidirectional actuator is mounted...... 51

3-3 A) Schematic of the full target speed profile for the SP and SPDT protocols. B) Visual feedback of target speed and current measured speed provided to participants while they were competing the task (no speed values were provided to participants, they are provided here for reference)...... 53

3-4 Full speed and exoskeleton power profile for Initial Exoskeleton Walking (protocol was identical on both days)...... 54

4-1 (A) Target speed profile and representative data. Each speed target lasted for 75 seconds, considered one experimental phase (phases are numbered for reference). Four periods of 0 m/s were included, each lasting 12 seconds. Representative actual speed data from the SPT trial of one participant is shown. (B) Visual feedback schematic. The arrow pointed to participants’ actual speed, which moved up and down next to the ribbon. The green region was centered at the target speed and ranged 0.1 m/s below and above the speed, with the yellow regions also ranging 0.1 m/s above and below the green region. Participants were not given absolute speed values...... 63

4-2 Green time proportion increased immediately following phase 1 and remained consistent throughout all subsequent phases in SPT with the exception of phase 7. Asterisk (*) indicates significant difference from all other phases. Hash mark (#) indicates significantly greater than phase 1 and significantly lower than all other phases...... 66

185 4-3 Secondary task response accuracy by cue type and speed. Asterisk (*) indicates significant pairwise differences; hash mark (#) indicates significantly less than tactile accuracy at standstill and 1.5 m/s...... 67

4-4 Secondary task response accuracy split by go/no-go (green versus red) cues. Average accuracy for green cues are stacked on top of average accuracy proportion for red cues at each walking speed for visual and tactile cues...... 68

4-5 Predicted values based on the GLME for green time proportion (GTP), Speed Ratio, Speed coefficient of variation (COV), and normalized rise time (NRT), the four metrics measured from treadmill speed during SP and SPDT. Error bars show standard error of predicted values incorporating impact of random effects, while the mean predicted value incorporates only fixed effects. . 70

4-6 Predicted values based on the GLME for normalized stride length (NSL), stride time (ST), and normalized stride width (NSW), the three gait characteristics during SP and SPDT. Error bars show standard error of predicted values incorporating impact of random effects, while the mean predicted value incorporates only fixed effects...... 71

5-1 Exoskeleton walking protocol overview and representative normalized stride length (NSL) data...... 83

5-2 Representative boxplots of gait characteristics (A. Normalized stride length; B. Normalized stride width) binned by walking condition. Only walking conditions at a speed of 1.3 m/s are shown. Participants were selected to highlight the variety of behaviors observed. Some participants showed no significant changes across any walking condition in NSL or NSW, such as EXO105 and EXO104, respectively. Some showed increases in NSL or NSW from baseline (B3) to early activated walking (A1), such as EXO111. Others showed decreased NSL during that time (e.g. EXO115). EXO111 showed gradual increases in NSL during adaptation (A1-A3) while EXO115 showed a larger increase from A1 to A2, followed by a decrease from A2 to A3. Participants also showed different variances in NSL and NSW (e.g. EXO104 shows larger variance in NSW than EXO107). Significant differences are not shown. Box- plots for all participants can be found in Appendix A.2...... 85

186 5-3 Pairwise comparisons of difference in normalized stride length (NSL) across selected walking conditions. A) Individual participant differences for each pair of walking conditions. Significant differences (p < 0.05) are marked by an asterisk. B) Values of Cohen’s d and effect size for each participant across pairwise comparisons when a significant difference was found. The positive or minus sign next to effect size denotes a positive or negative difference (i.e. positive means the first written condition of the pair had greater NSL, negative means the first condition showed a lower NSL). Colored boxes tothe left of participant numbers in B) indicate matched participant color bars in A). 87

5-4 Pairwise comparisons of difference in normalized stride width (NSW) across selected walking conditions. A) Individual participant differences for each pair of walking conditions. Significant differences (p < 0.05) are marked by anas- terisk. B) Values of Cohen’s d and associated effect size for each participant across pairwise comparisons when a significant difference was found. The positive or minus sign next to effect size denotes a positive or negative difference (i.e. positive means the first written condition of the pair had greater NSW, negative means the first condition showed a lower NSW). Colored boxes to the left of participant numbers in B) indicate matched participant color bars inA)...... 88

5-5 A) Normalized stride length (NSL) shown as averages across all participants for OFF-baseline conditions at four speeds (B1 – 0.5m/s, B2 – 1.0m/s, B3 – 1.3m/s, B4- 1.5m/s). Participants significantly increased NSL with speed to different magnitudes across both trials. Average participant NSL was significantly different for all speeds. B) Normalized stride width (NSW) boxplots for baseline speed bins for three representative participants. EXO104 showed no difference in NSW across speeds, while EXO113 showed widest NSW forthe slowest speed. EXO117 showed wider NSW at B1 and B2 (not significantly different from each other) than B3 and B4 (not significantly different from each other). Significant results from participant-specific ANOVA models are denoted. NSW boxplots for all participants can be found in Appendix A.2. . . 90

187 6-1 Example of lack of coordination or fluency with an exoskeleton. In the presence added power, if an individual activates muscles and plants their foot early, they are fighting the system rather than maintaining good coordination with the system...... 98

6-2 Table of gait priorities and associated changes in NSL and NSW during exoskeleton on and off power states. Up and down arrows indicate increases and decreases, respectively. Side arrows indicate no change. Circled X indicates undefined...... 99

6-3 Correlations between baseline parameters measured from the Simon task

(RTcon, RTincon, and ΔRT) and exoskeleton gait parameters...... 109

6-4 Correlations between mean GTP and Bias baseline parameters measured at 0.5 m/s during SP/SPDT and exoskeleton gait parameters...... 110

6-5 Selected correlations between dual task decrement (DTD) Bias parameters measured across SP/SPDT and exoskeleton gait parameters...... 111

6-6 Summary table of the possible relationships between baseline cognitive parameters and exoskeleton gait characteristics, with associated implications for shifting gait priorities...... 116

A-1 Normalized stride width box plots during baseline (un-powered exoskeleton before the exoskeleton was powered on) at four different speeds for subjects EXO103 to EXO110. B1: 0.5 m/s, B2: 1.0 m/s, B3: 1.3 m/s, B4: 1.5 m/s. Significant pairwise differences are marked with an asterisk below boxes. Asterisks above a box indicate that box is significantly different than all other boxes for the same subject...... 152

A-2 Normalized stride width box plots during baseline (un-powered exoskeleton before the exoskeleton was powered on) at four different speeds for subjects EXO111 to EXO118. B1: 0.5 m/s, B2: 1.0 m/s, B3: 1.3 m/s, B4: 1.5 m/s. Significant pairwise differences are marked with an asterisk below boxes. Asterisks above a box indicate that box is significantly different than all other boxes for the same subject...... 153

188 A-3 Normalized stride length box plots during all exoskeleton walking conditions at 1.3 m/s for subjects EXO103 to EXO108. Inset shows timing of the six walking conditions during the full exoskeleton walking protocol. Significant pairwise differences are not shown...... 154 A-4 Normalized stride length box plots during all exoskeleton walking conditions at 1.3 m/s for subjects EXO109 to EXO113. Inset shows timing of the six walking conditions during the full exoskeleton walking protocol. Significant pairwise differences are not shown...... 155 A-5 Normalized stride length box plots during all exoskeleton walking conditions at 1.3 m/s for subjects EXO114 to EXO118. Inset shows timing of the six walking conditions during the full exoskeleton walking protocol. Significant pairwise differences are not shown...... 156 A-6 Normalized stride width box plots during all exoskeleton walking conditions at 1.3 m/s for subjects EXO103 to EXO108. Inset shows timing of the six walking conditions during the full exoskeleton walking protocol. Significant pairwise differences are not shown...... 157 A-7 Normalized stride width box plots during all exoskeleton walking conditions at 1.3 m/s for subjects EXO109 to EXO113. Inset shows timing of the six walking conditions during the full exoskeleton walking protocol. Significant pairwise differences are not shown...... 158 A-8 Normalized stride width box plots during all exoskeleton walking conditions at 1.3 m/s for subjects EXO114 to EXO118. Inset shows timing of the six walking conditions during the full exoskeleton walking protocol. Significant pairwise differences are not shown...... 159 A-9 Exoskeleton gait and baseline comparisons 1-6 listed in order of presentation in Table 6.3...... 161 A-10 Exoskeleton gait and baseline comparisons 7-12 listed in order of presentation in Table 6.3...... 162 A-11 Exoskeleton gait and baseline comparisons 13-18 listed in order of presentation in Table 6.3...... 163 A-12 Exoskeleton gait and baseline comparisons 19-24 listed in order of presentation in Table 6.3...... 164

189 A-13 Exoskeleton gait and baseline comparisons 25-30 listed in order of presentation in Table 6.3...... 165 A-14 Exoskeleton gait and baseline comparisons 31-33 listed in order of presentation in Table 6.3...... 166

B-1 Testing locations on dominant leg for Semmes-Weinstein monofilament test. . 169 B-2 Birds eye view schematic of how the AAR test was implemented. All subjects were tested with their right foot; turning the ankle inward (toward 0o) constituted an internal rotation, turning the ankle outward (toward 180o constituted an external rotation...... 170 B-3 Varied speed exoskeleton walking (VSW) protocol details, completed by HESA study participants twice on Day 2. Individuals walked with the system for just under 25 minutes. The first four minutes of the VSW were identical to IEW, with one minute each at four different speeds with the system powered off. Then the exoskeleton was powered on and individuals walked forfive minutes at each of three walking speeds - 1.0, 1.3, and 1.5 m/s. Individuals did not walk with the system powered on at 0.5 m/s because the system did not actuate and provide assistance when walking at that speed. Individuals were then given five minutes to de-adapt at 1.3 m/s, as in the IEW protocol. 174

190 List of Tables

2.1 Output showing F-values, p-values, 휂2 values, and effect sizes for an ANOVA model on RT with four factors: subject, cue mode, response mode, and congruency. Significance at an alpha value of 0.05 is indicated by an asterisk in the p-value column. 휂2 values and effect sizes only shown when significance criteria were met. 40

2.2 Z-values, p-values, r values, and effect sizes for post-hoc pairwise Wilcoxon sign-rank tests on accuracy and response rate (RR). Significance at an alpha value of 0.05 is indicated by an asterisk in the p-value column. Values for r and effect sizes only shown when significance criteria were met. Boxplots of this data are presented in Figure 2-3...... 41

2.3 Cognitive processing time values calculated from mean RTs per subject and estimated values of total signal travel time for sensory and motor signals for each cue-response pair. Mean and standard deviation of observed RT for

each cue-response pair are presented for reference. Estimated Tcp for each cue-response pair was significantly different from all other cue-response pairs. 42

4.1 Dual task decrements calculated from model predicted values for each gait performance metric and gait strategy characteristic. Negative value indicates a lower predicted value in the presence of a dual task...... 69

4.2 Differences in predicted values between different target speeds for each gait performance metric and gait strategy characteristic. Negative value indicates lower predicted value at Speed 2 than at Speed 1...... 69

191 6.1 Table enumerating outcome variables from the modified Simon task and SP/SPDT trials associated with the exoskeleton walking protocol...... 101 6.2 Assessment of intra- versus inter-subject variability for all baseline parameters listed in Table 6.1. Results of Wilcoxon sign-rank tests on subject standard deviations compared to population standard deviation are presented. Pa- rameters with statistically significant differences in intra- versus inter-subject variability are denoted with an asterisk. Ratios of mean subject standard deviation to population standard deviation are also reported...... 106 6.3 Baseline and exoskeleton metric comparisons with significant Pearson’s r or Spearman’s 휌. Baseline metrics are all summary measures of the parameters from Table 6.2 which showed significance. Bias, GTP, and NSW across

SP/SPDT are pooled in mean metrics (e.g. 휇(Bias)all, 휇(GTP)1.3) while DTD metrics are the difference in mean value across SP and SPDT. Significant p- values at an 훼 level of 0.05 are bold...... 107

A.1 Coefficients estimated by a GLME incorporating seven DVs with categorical predictors Trial (1 - SP, 2 - SPDT), Speed (0.5, 1.0, 1.3, 1.5), DVtype (1 - GTP, 2 - ratio of mean measured to target speed, 3 - measured speed COV, 4 - NRT, 5 - NSL, 6 - NSW, 7 - ST), and random factor Subject. DF = 1725 for all estimates. Coefficients for each DV are grouped together. . . . 151

B.1 Table of all AAR trial internal/external rotation proportions. Actual angles must be calculated from measured maximal internal and external rotation. Proportion of maximal internal or external rotation are noted in this table (i.e. for trial 1, External 0.25, if maximal external rotation was 40 degrees, actual target angle would be External - 10 degrees)...... 171 B.2 All static balance trials ...... 172 B.3 Overview of split-belt walking trial ...... 173

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