Assistive Device For Elderly Rehabilitation: Signal Processing Techniques.

Abstract

With advancing age, the agility of the brain to process information critical for going about daily living slows down. As a result, persons affected by these disorders lose their dexterity, reflexes and speed in performing simple day-to-day tasks. The focus of this thesis is on developing algorithms for better processing of Electromyography (EMG) and Mechanomyography (MMG) signals for application in elderly rehabilitation. The problems investigated in this research are: a) Adaptive signal processing of the EMG signal to eliminate power line interference using Hilbert-Huang transform, b) Parameter Estimation of a Hybrid Muscle Model using an Iterative Learning Predictor for Estimation of the Joint Torque and c) Mechanomyography Feature Extraction and Classification of Forearm Movements using Empirical Mode Decomposition and Wavelet Transform The algorithms in this study follow real time constraints for assistive devices while the measurement protocols ensure that the bio-signals were broadly representative of that measured from the elderly.