Towards mechanomyographic control of multifunction upper-limb prostheses.
Published: 2006
Language: eng
Number of pages: 112
ISBN-13: 9780494211168
Description: Muscle vibrations, known as the mechanomyogram (MMG), have been used in a limited manner to control upper-limb prostheses. MMG is dependent on the recruitment and firing rates of muscle motor units during contraction. It was thus hypothesized that MMG signals would reflect distinctive patterns of muscle activation during the grasp of differently shaped objects. This thesis investigates this hypothesis, specifically focusing on forearm muscles. To this purpose, an innovative data collection methodology and vision-driven MMG segmentation algorithm are proposed. With seven able-bodied participants, MMG signal transients from three sites could be differentiated among three classes of forearm muscle activity with 78.6 +/- 13% accuracy using wavelet packet-derived features and a linear classifier. Furthermore, steady-state characterization indicated significant differences in stationarity test statistic values among MMG signals corresponding to different grasps (p&lt;0.01). Collectively, these results suggest that with additional research, MMG may indeed become a usable control signal for multifunction prostheses.
Notes: Source: Masters Abstracts International, Volume: 45-03, page: 1587.

Advisor: Tom Chau.

Thesis (M.A.Sc.)--University of Toronto, 2006.

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