Article

The performance of pattern recognition based myoelectric control has seen
significant interest in the research community for many years. Due to a recent
surge in the development of dexterous prosthetic devices, determining the
clinical viability of multifunction myoelectric control has become paramount.
Several factors contribute to differences between offline classification accuracy
and clinical usability, but the overriding theme is that the variability of the
elicited patterns increases greatly during functional use. Proportional control
has been shown to greatly improve the usability of conventional myoelectric
control systems. Typically, a measure of the amplitude of the electromyogram (a
rectified and smoothed version) is used to dictate the velocity of control of a
device. The discriminatory power of myoelectric pattern classifiers, however, is
also largely based on amplitude features of the electromyogram. This work
presents an introductory look at the effect of contraction strength and
proportional control on pattern recognition based control. These effects are
investigated using typical pattern recognition data collection methods as well as
a real-time position tracking test. Training with dynamically force varying
contractions and appropriate gain selection is shown to significantly improve
(p<0.001) the classifier's performance and tolerance to proportional control.

Langue : ANGLAIS