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Wearable Sensor System for Human Localization and Motion Capture

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Wearable Sensor System for Human Localization and Motion Capture Wearable Sensor System for Human Localization and Motion Capture by Shaghayegh Zihajehzadeh M.Sc., Amirkabir University of Technology, 2011 B.Sc., Isfahan University of Technology, 2008 Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in the School of Mechatronic Systems Engineering Faculty of Applied Sciences Shaghayegh Zihajehzadeh 2017 SIMON FRASER UNIVERSITY Spring 2017 Approval Name: Shaghayegh Zihajehzadeh Degree: Doctor of Philosophy Title: Wearable Sensor System for Human Localization and Motion Capture Examining Committee: Chair: Gary Wang Professor Edward J. Park Senior Supervisor Professor Stephen Robinovitch Supervisor Professor Kevin Oldknow Supervisor Senior Lecturer Carolyn Sparrey Internal Examiner Associate Professor Benny Lo External Examiner Lecturer Department of Surgery and Cancer Imperial College London Date Defended/Approved: April 27, 2017 ii Ethics Statement iii Abstract Recent advances in MEMS wearable inertial/magnetic sensors and mobile computing have fostered a dramatic growth of interest for ambulatory human motion capture (MoCap). Compared to traditional optical MoCap systems such as the optical systems, inertial (i.e. accelerometer and gyroscope) and magnetic sensors do not require external fixtures such as cameras. Hence, they do not have in-the-lab measurement limitations and thus are ideal for ambulatory applications. However, due to the manufacturing process of MEMS sensors, existing wearable MoCap systems suffer from drift error and accuracy degradation over time caused by time-varying bias. The goal of this research is to develop algorithms based on multi-sensor fusion and machine learning techniques for precise tracking of human motion and location using wearable inertial sensors integrated with absolute localization technologies. The main focus of this research is on true ambulatory applications in active sports (e.g., skiing) and entertainment (e.g., gaming and filmmaking), and health-status monitoring. For active sports and entertainment applications, a novel sensor fusion algorithm is developed to fuse inertial data with magnetic field information and provide drift-free estimation of human body segment orientation. This concept is further extended to provide ubiquitous indoor/outdoor localization by fusing wearable inertial/magnetic sensors with global navigation satellite system (GNSS), barometric pressure sensor and ultra-wideband (UWB) localization technology. For health applications, this research is focused on longitudinal tracking of walking speed as a fundamental indicator of human well-being. A regression model is developed to map inertial information from a single waist or ankle- worn sensor to walking speed. This approach is further developed to estimate walking speed using a wrist-worn device (e.g., a smartwatch) by extracting the arm swing motion intensity and frequency by combining sensor fusion and principal component analysis. Keywords: inertial/magnetic sensor; orientation estimation; position estimation; walking speed; Kalman filter; Gaussian process regression. iv Dedication I dedicate this work to my caring husband and devoted parents. v Acknowledgements I would like to thank my senior supervisor, Dr. Edward Park, for providing invaluable mentorship, technical expertise and encouragement throughout my PhD studies. His support, inspiration, professional guidance and patience helped me enjoy every moment of working on this thesis. I would also like to thank my supervisory committee: Dr. Stephen Robinovitch and Dr. Kevin Oldknow for providing expert opinion and evaluating different parts of this research. I am also thankful to Dr. Farid Golnaraghi for his confidence in me and accepting me to this PhD program and guiding me during the first few months of my studies. I would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for the financial support through Vanier Canada Graduate Scholarship (CGS) program. I am grateful to my lab colleagues and friends: Dr. Omar Aziz, Dr. Ahmed Arafa, Darrell Loh, Matthew Lee, Paul Yoon, Magnus Musngi, Dr. Majid Shokoufi, Shervin Jannesar, and Behzad Abdi. I am glad I had the opportunity to meet and spend time with these amazing people. Many thanks go to my lovely friends Nastaran Hajinazar, Amir Pourmand and Rajesh Rao for their unconditional friendship and support, making my life more joyful. Family isn’t always blood; you are my family in Canada. My special thanks go to my caring husband, Ramin who motivated me in so many ways. There are no words that can express my gratitude and appreciation for all you have done and been for me. Your continued and unfailing love, support and understanding made the completion of this thesis possible. I am thoroughly thankful to my parents and parents-in-law for their encouragement and unwavering belief in me. vi Table of Contents Approval .......................................................................................................................... ii Ethics Statement ............................................................................................................ iii Abstract .......................................................................................................................... iv Dedication ....................................................................................................................... v Acknowledgements ........................................................................................................ vi Table of Contents .......................................................................................................... vii List of Acronyms ............................................................................................................. ix Chapter 1. Introduction ............................................................................................. 1 1.1. Background and Motivation .................................................................................... 1 1.2. Objectives ............................................................................................................... 2 1.3. Organization of the Dissertation .............................................................................. 3 Chapter 2. Literature Review ..................................................................................... 5 2.1. MoCap of human body segments ........................................................................... 5 2.2. Indoor and outdoor localization ............................................................................... 7 2.3. Walking speed estimation using wearable IMU ..................................................... 10 Chapter 3. Summary of Contributions .................................................................... 13 3.1. Estimation of Human Body Segment Orientation .................................................. 13 3.1.1. A cascaded two-step KF for estimation of human body segment orientation using wearable IMU ............................................................... 13 3.2. Tracking of Position and Velocity Trajectories ...................................................... 14 3.2.1. Integration of MEMS inertial and pressure sensors for vertical trajectory determination ........................................................................... 14 3.2.2. A cascaded Kalman filter-based GPS/MEMS-IMU integration for sports applications ................................................................................... 15 3.2.3. UWB-aided inertial motion capture for lower body 3-D dynamic activity and trajectory tracking .................................................................. 16 3.3. Magnetometer-free Motion Tracking ..................................................................... 18 3.3.1. A magnetometer-free indoor human localization based on loosely coupled IMU/UWB fusion ......................................................................... 18 3.3.2. A novel biomechanical model-aided IMU/UWB fusion for magnetometer-free lower body motion capture ........................................ 19 3.4. Regression Model-based Walking Speed Estimation ............................................ 20 3.4.1. Experimental evaluation of regression model-based walking speed estimation using lower body-mounted IMU .............................................. 20 3.4.2. Regression model-based walking speed estimation using wrist- worn inertial sensor ................................................................................. 21 Chapter 4. Conclusions and Future Work .............................................................. 23 4.1. Conclusions and Contributions ............................................................................. 23 vii 4.2. Future Work .......................................................................................................... 25 References ................................................................................................................ 27 Appendix A. A cascaded two-step Kalman filter for estimation of human body segment orientation using MEMS-IMU ......................................................... 35 Appendix B. Integration of MEMS inertial and pressure sensors for vertical trajectory determination ........................................................................................ 40 Appendix C. A cascaded Kalman filter-based GPS/MEMS-IMU integration for sports applications ..........................................................................................
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