Date of Award

9-4-2015

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Systems Engineering

First Advisor

Andrew Wright

Second Advisor

Kamran Iqbal

Abstract

Lower limbs enable human beings to move in an uncomplicated manner. They support the weight of the body, provide postural stability while moving the body forward and even move it backwards. Regardless of the environment these limbs have the capacity to adapt to any terrain. However, disability as a result of amputation may lead a person to live a dismal life. The deficiency of the ability to walk can have undesirable effect on the amputee from both the physical and psychological point of view. Use of prosthetic devices helps in overcoming this deficiency. The use of prostheses goes back to ancient times when prosthetic limbs in the form of peg legs were used. In recent past, these artificial limbs have taken a more refined form i.e. from simple passive devices (contributing no net power) to active devices (contributing net power). Optimum utilization of these devices relies upon identifying user intent for locomotion mode identification. The objective of the thesis is to present two methodologies to identify the locomotion modes for active above knee prostheses. The first methodology utilized kinematic information for the hip joint along with the myoelectric information from the muscles. The results indicated that the kinematic information along with the neuromuscular information significantly decreased (p<0.05) the classification error as compared to using only neuromuscular information. The second methodology evaluated the hypothesis, that the notion of muscle synergies is capable of classifying locomotion modes. The hypothesis was evaluated for the weight bearing and non-weight bearing movements. Offline data analysis was performed for the weight bearing movements while both offline data analysis and real-time evaluation of the hypothesis was done for non-weight bearing movements. The results suggest that the notion of muscle synergies is effective for lower limb locomotion mode identification.

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