Date of Award

2017

Document Type

Open Access Master's Report

Degree Name

Master of Science in Mechanical Engineering (MS)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

Mo Rastgaar

Committee Member 1

Nina Mahmoudian

Committee Member 2

Ye Sun

Committee Member 3

Timothy Havens

Abstract

For millions of people, mobility has been afflicted by lower limb amputation. Lower extremity prostheses have been used to improve the mobility of an amputee; however, they often require additional compensation from other joints and do not allow for natural maneuverability. To improve upon the functionality of ankle-foot prostheses, it is necessary to understand the role of different muscle activations in the modulation of mechanical impedance of a healthy human ankle. This report presents the results of using artificial neural networks (ANN) to determine the functional relationship between lower extremity electromyography (EMG) signals and ankle impedance in the transverse plane. The Anklebot was used to apply pseudo-random perturbations to the human ankle in the transverse plane, while motion of the ankle in the sagittal and frontal planes was constrained. Using a stochastic system identification method, the mechanical impedance of the ankle in external-internal (EI) direction was determined as a function of the applied torque and corresponding ankle motion. The impedance of the ankle and muscle EMG signals were determined for three muscle activation levels, including with relaxed muscles, and with muscles activated and 10% and 20% of the subject’s maximum voluntary contraction (MVC). This information was used as the input and target matrices to train an ANN for each subject. The resulting ankle impedance from the proposed ANN was effectively predicted within 85% accuracy for nine out of ten subjects, and was within ±5 Nm/rad of the target impedance for all subjects. This work provides more understanding of the neuromuscular characteristics of the ankle and provides insight toward future design and control of ankle-foot prostheses.

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