Date of Award

12-23-2014

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Systems Engineering

First Advisor

Kamran Iqbal

Abstract

Control of powered prostheses using the information from leftover muscles in amputees is a challenging problem. I investigated the problem with a view to find clinically reliable and robust schemes that can interpret human intent and control a powered prosthesis in real-time. Recently, pattern classification systems have been suggested to map muscle activation patterns to the human intent (or the movement). However, due to the underlying generic structure, system-specific information cannot be exclusively incorporated into pattern classification algorithms. I have proposed two new methodologies in my research. The first one extended the pattern classification with a new feature set comprised of autoregressive-autoregressive generalized conditional heteroscedastic (AR-GARCH) coefficients, that outperformed the conventional feature set (p . The second methodology addressed the problem by suggesting a novel physiologically-relevant mathematical model. The hypothesis of muscle synergies was employed to formulate a state-space representation of the neural signals. The proposed model is based on the assumption that the neural drive originating from the central nervous system (CNS) and terminating at the peripheral muscles contains task-specific information. Therefore, the neural drive was estimated accurately using a recursive Bayesian technique. Later, the estimated neural drive and task-specific muscle synergies were used to identify the intended/performed task. Detailed performance analysis was performed in off-line as well as real-time using a virtual prosthesis. The proposed scheme outperformed the pattern classification systems in off-line accuracy (p and real-time controllability (p .

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