University of Texas at El Paso DigitalCommons@UTEP Open Access Theses & Dissertations 2010-01-01 Categorization of Functional Impairments in Human Locomotion using the Methods of the Fusion of Multiple Sensors and Computational Intelligence Huiying Yu University of Texas at El Paso, [email protected] Follow this and additional works at: https://digitalcommons.utep.edu/open_etd Part of the Biomedical Commons, and the Electrical and Electronics Commons Recommended Citation Yu, Huiying, "Categorization of Functional Impairments in Human Locomotion using the Methods of the Fusion of Multiple Sensors and Computational Intelligence" (2010). Open Access Theses & Dissertations. 2814. https://digitalcommons.utep.edu/open_etd/2814 This is brought to you for free and open access by DigitalCommons@UTEP. It has been accepted for inclusion in Open Access Theses & Dissertations by an authorized administrator of DigitalCommons@UTEP. For more information, please contact [email protected]. CATEGORIZATION OF FUNCTIONAL IMPAIRMENTS IN HUMAN LOCOMOTION USING THE METHODS OF THE FUSION OF MULTIPLE SENSORS AND COMPUTATIONAL INTELLIGENCE HUIYING YU Department of Electrical and Computer Engineering APPROVED: ________________________________ Thompson Sarkodie-Gyan, Ph.D., Chair ________________________________ Scott Starks, Ph.D. ________________________________ Richard Brower, M.D. ________________________________ Bill Tseng, Ph.D. ________________________________ Eric Spier, M.D. __________________________________ Patricia D. Witherspoon, Ph.D. Dean of the Graduate School Copyright © by Huiying Yu 2010 CATEGORIZATION OF FUNCTIONAL IMPAIRMENTS IN HUMAN LOCOMOTION USING THE METHODS OF THE FUSION OF MULTIPLE SENSORS AND COMPUTATIONAL INTELLIGENCE by HUIYING YU, MPHIL DISSERTATION Presented to the Faculty of the Graduate School of The University of Texas at El Paso in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Department of Electrical and Computer Engineering THE UNIVERSITY OF TEXAS AT EL PASO August 2010 Acknowledgements I would never have been able to finish my dissertation without the guidance of my committee members, help from friends and colleagues, and support from my family. First and foremost I would like to express the deepest appreciation to my committee chair, Dr. Thompson Sarkodie-Gyan, who has the attitude and the substance of a genius: he continually and convincingly conveyed a spirit of adventure in regard to research and scholarship, and an excitement in regard to teaching. Without his guidance and persistent help this dissertation would not have been possible. One simply could not wish for a better or friendlier supervisor. I would like to thank one of my committee members, Dr. Richard Brower, who always gave the greatest support: he provided me funding which made it possible to attend one of the most important IEEE conferences in the area of rehabilitation (ICORR, 2009, Japan); he also provided Lab equipment, an electromyographic device (EMG) to support this research. Dr. Brower also introduced many participants including some of the neurological impaired subjects to me. I would further like to thank Dr. Amr Abdelgawad, Dr. Eric Spear, Dr Steven Glusman, and Mr. James Moody for providing mobility-related impaired subjects as participants to my research. I would also like to extend sincere thanks to Dr. Scott Starks and Dr. Bill Tseng for guiding my PhD research study over the past years. I would like to express my special thanks to them for their membership of my dissertation committee that always supported me from the beginning of the proposal to the final defense. In my daily work I have been blessed with a friendly and cheerful group of student colleagues. Murad Alaqtash, Chad McDonald (graduated in December 2009), Oscar Espino, Luis Sagarnaga, Julio Torres, all helped me with the equipment set-up and data collection. Without their help, this dissertation could not contain the enriched information from so much data. iv This dissertation was performed and accomplished six months ahead of time. Instead of completing the dissertation in December, it was finally done on June 29, 2010. This was possible through the financial support obtained from the Stern Foundation. I am deeply indebted to the Stern Foundation for this great cooperation ad assistance. v Abstract The main aim of this dissertation work was to develop an intelligent system to monitor, quantify and differentiate variances in human gait with high reliability and efficiency using the fusion of multiple sensor data and the methods of fuzzy inferential logic. Gait disorders are heterogeneous and produce disabilities that vary substantially from individual to individual. The recognition, quantification and analysis of gait dysfunction is complex and, requires the integration of large amounts of data across multiple domains (kinetic, kinematic and electromyographic). Current systems for gait analysis generally require space and complex imaging equipment, as well as prolonged processing time, rendering them unsuitable for real-time applications. Quantitative gait analysis has been used to elucidate characteristic features of neurological gait disturbances. Although a number of studies have compared single patient groups with controls, there are only a few studies comparing gait parameters between patients with different neurological disorders. This dissertation work is based on the hypothesis that functional rehabilitation can be most effectively achieved through the reduction of variances from normal patterns through training and other compensatory strategies, hence, efficient and reliable detection, quantification and differentiation of these variances is a critical link between diagnosis and optimal recovery. Current clinical methods of gait analysis are time and labor intensive and involve extensive post- hoc data analysis. These limitations reduce access to gait analysis and exclude direct application of the patient’s gait data to rehabilitative interventions in real-time. The goal of the dissertation work was to develop a novel intelligent system to monitor, quantify and differentiate variances in human gait with high reliability and efficiency using the fusion of multiple sensor data and the methods of fuzzy inferential logic. vi Applications of this innovative technology will include improved recognition of complex patterns related to variable and combined pathophysiologic factors, and reliable quantitative monitoring of gait-related disability with recovery or therapeutic intervention over time. vii Table of Contents Acknowledgements ................................................................................................ iv Abstract .................................................................................................................. vi Table of Contents ................................................................................................ viiii List of Tables ........................................................................................................ xii List of Figures ...................................................................................................... xiii Chapter 1: Introduction ............................................................................................1 1.1 Background and Significance ................................................................5 1.2 Motivation ............................................................................................15 1.3 Specific Aims .......................................................................................16 1.4 Overview of this Dissertation ..............................................................18 Chapter 2: Global Burden of Neurological Disorders ...........................................19 2.1 The Global Burden Awareness for Neurological Disorders ................19 2.2 Stroke ...................................................................................................20 2.2.1 Effects of the Stroke....................................................................21 2.2.2 Burden of the Stroke ...................................................................21 2.2.3 Treatment and Rehabilitation of Stroke Survivors .....................22 2.3 Multiple Sclerosis ................................................................................23 2.3.1 Effects of Multiple Sclerosis .......................................................24 2.3.2 Types of Multiple Sclerosis ........................................................25 2.3.3 Burden of Multiple Sclerosis ......................................................26 2.3.4 Treatment and Neurorehabilitation of Multiple Sclerosis ..........27 2.4 Spinal Cord Injury................................................................................28 2.4.1 Location and Effects of Spinal Cord Injury ...............................30 2.4.2 Classification of Spinal Cord Injury ...........................................31 2.4.3 Incidence, Prevalence and Consequence of SCI .........................32 2.4.4 Treatment and Rehabilitation of Spinal Cord Injury ..................34 2.5 Cerebral Palsy ......................................................................................37 2.5.1 Classification of Cerebral Palsy ..................................................38 2.5.2 Prevalence and Incidence of Cerebral Palsy ...............................39 viii 2.5.3 Treatments and Rehabilitation of Cerebral Palsy .......................40