Date of Award

4-27-2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Systems Engineering

First Advisor

Kamran Iqbal

Abstract

Muscle synergies (MS) are defined as the specific co-activation of muscle groups to accomplish functional movements. The hypothesis of muscle synergies helps simplify the motor coordination problem by decreasing the numbers of central nervous system signals. The purpose of this research was to investigate the number and the structure of muscle synergies for the upper limb 1) across participants for one power level 2) when the power levels increased and at a constant speed [50 r.p.m] and 3) including the synergies for pull and push for each participant during arm cycling on a stationary hand-cycle ergometer. We asked eight healthy participants to accomplish cycling rotational motion by hand-cycle ergometer to investigate the number and the structure of muscle synergies across participants, the results across power levels with constant speed, and the structure comparison of muscle synergies between pull and push for each participant. We acquired Electromyography (EMG) signals from seven right upper-limb muscles of eight participants as they operated the hand-cycle ergometer. Non-negative matrix factorization (NNMF) was used to extract muscle synergies from the EMG signals for analyses. Each time series contained data for 22 cycles. The EMG signal was preprocessed and averaged across cycles. Cosine similarity was used to compare the MS, and cross-correlation was used to compare activation coefficients. We found that five synergies were shared across participants for each power level, and a total of three shared MS explaining ≥ 95% of the variance accounted for (VAF) across different power levels. We found three biomechanical functions of three shared synergies. We investigated the structure of MS for pull and push. We used 13 cameras for motion capture to identify the pull and the push for each participant. We found one MS for pull, two MS for push, and one shared MS between pull and push. Also, the recruitment profile for those synergies appeared to be similar with only slight deviation. We found that the number and structure of synergies was consistent across participants and across power levels due to modulation in their activation coefficients. This study shows that MS analysis as a quantitative tool is crucial for understanding motor behavior, as well as for developing assistive technologies for rehabilitation. Intuitive myoelectric assistive technology (AT) control may be achieved if the control of myoelectric AT is based on neuromotor control principles because myosignals are produced via a limited number of fixed or consistent muscle synergies.

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