Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/38799
Title: Assistive Device For Elderly Rehabilitation: Signal Processing Techniques.
Authors: SANGIT SASIDHAR
Keywords: Mechanomyography, Electromyography, Iterative Learning Predictor, Hilbert-Huang Transform, Empirical Mode Decomposition, Wavelet Transform
Issue Date: 29-Jan-2013
Source: SANGIT SASIDHAR (2013-01-29). Assistive Device For Elderly Rehabilitation: Signal Processing Techniques.. ScholarBank@NUS Repository.
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.
URI: http://scholarbank.nus.edu.sg/handle/10635/38799
Appears in Collections:Ph.D Theses (Open)

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