Developing a User-Independent Deep Learning-Based Biomechanical Gait Analysis System Using Full Body Kinematics and Electromyography

Author

Goksu Avdan, Southern Illinois University CarbondaleFollow

Date of Award

8-1-2024

Degree Name

Doctor of Philosophy

Department

Engineering Science

First Advisor

Onal, Sinan

Second Advisor

Lu, Chao

Abstract

Motion capture (mocap) systems integrated with force plates and electromyography (EMG) collect detailed kinematic and kinetic data on subjects, including stride length, width, cadence, speed, and other spatiotemporal parameters. These systems allow clinicians and researchers to analyze movements, both cyclic (e.g., walking, running) and non-cyclic (e.g., jumping, falling), which is crucial for understanding movement patterns and identifying abnormalities. Clinical gait analysis, a key application, focuses on detecting musculoskeletal issues and walking impairments. While essential for diagnosing gait disorders and planning interventions, clinical gait analysis faces challenges such as noise, outliers, and marker occlusion in optical motion tracking data, requiring complex post-processing. Additionally, the measurement of ground reaction forces (GRFs) and moments (GRMs) is limited due to the restricted number of force plates. There are also challenges in EMG data collection, such as finding optimal MVC positions and developing nonlinear normalization techniques to replace traditional methods.To address these challenges, this research aims to develop an AI-driven gait analysis system that is cost-effective, user-independent, and relies solely on kinematic and EMG data for real-time analysis. The system is specifically designed to assess musculoskeletal characteristics in individuals with special needs, walking disabilities, or injuries, where measuring MVC levels is impractical or unsafe. The research has four main objectives: (1) standardize MVC positions for four lower limb muscles, (2) develop an alternative EMG normalization technique using nonlinear data analysis, (3) create an unsupervised framework using transformers for missing marker recovery without perfect ground-truth data, and (4) generate GRFs, GRMs, and JMs from lower limb kinematics using a 1D-CNN, improving accuracy and efficiency with transfer learning, without requiring force plates. While addressing these challenges, the proposed system aims to minimize user interaction, reduce pre- and post-processing, and lower costs for researchers and clinicians. The designed tool will integrate with existing optical marker-based mocap systems, providing greater flexibility and usability. In educational settings, it will offer students hands-on experience in advanced gait analysis techniques. Economically, widespread adoption of the tool in research and clinical settings will reduce data collection and analysis costs, making advanced gait analysis more accessible. Additionally, this tool can be applied to other fields, such as precision manufacturing, security, and predictive maintenance, where analyzing data can predict failures. Consequently, this research will significantly advance the field of human movement, increasing the volume and quality of research using optical marker-based mocap systems.