1.1 Human System Interaction Thrust

2011 Annual Report – Volume 2

2011 Annual Report 2011 Annual Report – Volume 2

Volume 2

2 2011 Annual Report – Volume 2

1. Core Projects

1.1 Human System Interaction Thrust

1.2 Mobility and Manipulation Thrust

1.2.1 Understanding Humans

ERC Team Members Lead: Hartmut Geyer (CMU RI) PhD Student: Sarah Neyer (CMU ME)

Goals Although several models for swing leg mechanics have been proposed, no comprehensive forward-dynamic model of neuromuscular swing-leg control exists. Identifying such a model will have a major impact on understanding human gait changes and dynamic balance restoration following slip, trip or similar disturbances occurring in normal locomotion activities.

Role in Support of QoLT Strategic Plan Neuromuscular models will support future therapy and rehabilitation coaches, as well as systems that reduce the risk of accidents.

Fundamental Research Barriers; Methodologies to Address Them It remains unclear what the underlying principles are that generate muscle actuation stabilizing legged locomotion. We seek to understand these principles and to identify the corresponding sensorimotor control in humans. We approach this problem from two complementary directions (Fig. 1). First, we are developing principled models of dynamic balance control in locomotion. Dynamic balance guarantees gait stability. It requires adapting foot placement targets in swing to changing center of mass dynamics as they occur after disturbances. Second, we are developing actuated swing leg models that attract the chaotic pendulum-chain dynamics into these foot placement targets. Ultimately, we will map the required joint actuations onto the sensorimotor control of human leg muscles using muscle-reflex models.

Achievements Dynamic Balance Control We have extended the linear inverted pendulum model to a bipedal system which includes double support dynamics, and have derived a reactive balance controller based on foot placement targets and double stance length (Fig. 2). We have found that this controller enables the model to stand and walk at user-defined target speeds, to transition between these behaviors by acceleration and deceleration, and to react to intermittent disturbances and compensate for permanent ones, as long as they are compatible with the swing leg dynamics placing the feet. We will publish this work at ICRA 2011 (Parietti and Geyer, accepted).

Actuated Swing Leg Models To understand how the human swing leg motion can be forced into foot placement targets, we are exploring nonlinear mono- and biarticular spring elements that span the leg joints. So far, we have identified with a compound pendulum model spring elements that are essential to force the swing motion into target points (Fig 3). Our preliminary findings also show that the resulting shape of the foot point trajectory is similar to shape of the ankle trajectory in human walking and running at different speeds.

The essential nonlinear spring elements are detailed in Figure 4, which shows how the potential energy field of the foot point in the compound pendulum model is transformed to ensure targeted swing leg motions. The left-top panel shows the potential energy field U_pot of the passive compound pendulum’s foot point. The right-bottom panel shows the transformed potential energy field U_tot after adding nonlinear, mono- and biarticular springs that represent major leg muscles. The remaining panels show the individual potential field contributions of muscle springs, including hamstring (U_ham), satorius (U_sat), rectus femoris (U_rf), hip flexor (U_hfl), and vastii (U_vas). U_hk and U_gvs are spring potential fields that do not have equivalent muscles but were important to prevent foot drop (U_hk) and knee overshoot (U_gvs).

Relevant Work Being Conducted Elsewhere; How this Project Differs Human swing leg dynamics are based on pendulum chain dynamics (Mochon & McMahon, 1980). Without actuation, pendulum chains move in chaotic ways; by contrast, experiments show that animal and human legs produce well coordinated and fluid motions in swing stabilizing locomotion even after large disturbances including trips and unexpected drops in ground level (Daley et al., 2006). In line with this observation, inverse dynamics analysis of human walking data reveals that, although humans take advantage of passive motions, muscle actuation is an integral part of swing dynamics (Whittlesey et al., 2000). Dynamic balance strategies have mainly been developed using the linear inverted pendulum model as theoretical framework (Kajita et al., 1990; Pratt et al., 2006). While this model can identify single leg strategies for balance control, it does not consider the double support that is common to bipedal locomotion, indicating that current strategies do not fully exploit the theoretical potential of balance control in bipedal systems. We have extended the linear inverted pendulum model to a bipedal system which includes double support dynamics, and have derived a reactive balance controller based on foot placement targets and double stance length.

Expected Deliverables and Milestones Simulation of planar human walking, slipping, and tripping (Year 6) Simulation of 3D human walking, slipping, and tripping (Year 7) Models & simulations of how accidents happen (Year 8 and following)

Contributions and Broader Impact We expect our work to improve the theoretical understanding of basic locomotion behaviors, to identify neuromuscular models of behavior representation that make testable predictions about the sensorimotor control of humans in steady and disturbed locomotion, and (iii) to further the electromechanical design and control of actuated prosthetic and orthotic devices.

Future Plans General focus will be on better theories of human speed changes, gait transitions and dynamic balance in 3D locomotion. We aim to develop behavior models and neuromuscular models of unprecedented dexterity. In particular, for CY2011 we will focus on extending our reactive leg placement control to 3D locomotion, on generalizing our swing-leg control for arbitrary foot placement targets, and on mapping the two behavior models onto the sensorimotor control of our planar neuromuscular models. As a result, we expect predictions about human muscle control during slipping and tripping.