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An IR and RF Based System for Functional Gait Analysis in a Multi-Resident Smart-Home

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An IR and RF Based System for Functional Gait Analysis in a Multi-Resident Smart-Home Portland State University PDXScholar Dissertations and Theses Dissertations and Theses Winter 4-4-2017 An IR and RF Based System for Functional Gait Analysis in a Multi-Resident Smart-Home Erich Reinhardt Schafermeyer Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/open_access_etds Part of the Electrical and Computer Engineering Commons Let us know how access to this document benefits ou.y Recommended Citation Schafermeyer, Erich Reinhardt, "An IR and RF Based System for Functional Gait Analysis in a Multi- Resident Smart-Home" (2017). Dissertations and Theses. Paper 3502. https://doi.org/10.15760/etd.5386 This Thesis is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. An IR and RF Based System for Functional Gait Analysis in a Multi-Resident Smart-Home by Erich Reinhardt Schafermeyer A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Electrical and Computer Engineering Thesis Committee: Eric Wan, Chair Melanie Mitchel James McNames Portland State University 2017 Abstract Changes in the gait characteristics, such as walking speed and stride length, of a person living at home can be used to presage cognitive decline, predict fall potential, monitor long-term changes in cognitive impairment, test drug regimens, and more. This thesis presents a novel approach to gait analysis in a smart-home environment by leveraging new advances in inexpensive sensors and embedded systems to create novel solutions for in-home gait analysis. Using a simple, non-invasive hardware system consisting entirely of wall-mounted infrared and radio frequency sensor arrays, data is collected on the gait of subjects as they pass by. This data is then analyzed and sent to a clinician for further study. The system is non-invasive in that it does not use cameras and could be built into the molding of a home so that it would be nearly invisible. In a finished prototype version, the system presented in this thesis could be used to analyze the gait characteristics of one or more subjects living in a home environment while ignoring the data of visitors and other non-subject cohabitants. The ability to constantly collect data from a home environment could provide thousands of observations per year for clinical analysis. Providing such a robust data set may allow people with gait impairment to live at home longer and more safely before transitioning to a care facility, have a reduced fall risk due to better prediction, and live a healthier life in old age. i To Mary Beth Llorens and Howard Newman, my parents, who have always encouraged and supported me in finding my own path. Thank you. ii Acknowledgements I would like to acknowledge the many people who contributed to the research documented in this thesis. Dr. Eric Wan was my thesis advisor, primary investigator, and boss who saw me through this process from beginning to end. Fatima Adenwala, Shadman Samin, Tanisha Payne, Colin Doolittle, Jamie Wong, Noah Zentzis, Walter Woods, and the many other student contributors in the lab were invaluable. Dr. Peter Jacobs, Jon Folsom, John Condon, David Edwards, Anindya Paul, Nick Preiser, and the many people at EmbedRF got me off to a great start. Dr. James McNames, Dr. Melanie Mitchel, Dr. Bob Bass, Mark Faust, Dean Ren Su, and the many faculty members facilitated my learning and growing as an engineer while at Portland State University. This work could not have been completed without the hardware development skills of Shadman Samin, with whom I worked on all aspects of the data collection hardware. Shadman is directly responsible for prototypes 1 and 3 (the next one) of the gait analysis system. He is a longtime contributor to the lab and a valuable asset to the university. Tanisha Payne, Eric Wan, Jon Condon Fatima Adenwala, and Shadman Samin were all instrumental in the data collection process. Each often spent hours of his or her weekend in the lab walking back and forth, running data collections, hauling hardware to and from a data collection site, and debugging the many aspects of the system. Hardware development was greatly aided by the PCB design skills of Jon Folsom who taught me to design PCBs correctly. I would like to thank him for his patience and iii expertise in hardware and circuit design and debugging. The data collection software for the subject ID systems was designed and developed by Nick Preiser at OHSU/EmbedRF, who is a wizard. RF Sensor firmware was originally written by Peter Jacobs and further developed by Jon Folsom. All of the algorithms, digital signal processing software, and data flow designs were done under the advisement of Eric Wan and Anindya Paul who were instrumental in helping me to understand the complex inner workings of the many statistical methods attempted in the pursuit of this research. Similarly, all credit for machine learning understanding goes to Melanie Mitchel and Eric Wan for helping in the design, debugging, implementation and analysis of the machine learning algorithms used herein. Thanks also to Dean Ren Su for making graduate school more fun and being a wonderful human. Thank you all for an interesting, trying, fun, hard, detailed, painstaking, rewarding experience. It has been a pleasure. You guys rock, I couldn’t have done it without you. iv Table of Contents Abstract ............................................................................................................................................ i Acknowledgements ....................................................................................................................... iii List of Tables ................................................................................................................................ vii List of Figures .............................................................................................................................. viii Introduction .................................................................................................................................... 1 Chapter 1: Motivation ................................................................................................................... 4 Aging, Cognitive Decline, and the American Healthcare System .......................................... 4 The Link between Gait and Health .......................................................................................... 6 Current Methods of Gait Analysis ........................................................................................... 9 The Vicon Camera System .................................................................................................. 14 Radio Frequency Antennae Array ..................................................................................... 15 The Opal System by APDM ................................................................................................ 16 The Kinect by Microsoft ...................................................................................................... 17 The Benefits of In-Home Gait Analysis .............................................................................. 18 Current In-Home Gait Analysis Systems............................................................................... 19 The GAITRite Walkway ..................................................................................................... 20 ORCA Tech PIR system ...................................................................................................... 21 The IR Distance Sensor System .......................................................................................... 22 Chapter 2: Design Methodology ................................................................................................. 27 Hardware .................................................................................................................................. 30 The Sensor ............................................................................................................................ 32 The Mounting System .......................................................................................................... 33 The Control Circuit ............................................................................................................. 34 Ideal Data Example .............................................................................................................. 37 Software .................................................................................................................................... 39 Data Pre-processing ............................................................................................................. 40 Algorithms ............................................................................................................................ 41 Data Processing .................................................................................................................... 49 v Chapter 3: Results and Discussion ............................................................................................. 53 Results ....................................................................................................................................... 55 Stationary Foot Detection ...................................................................................................
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