Article

Pattern recognition based control of powered upper limb myoelectric prostheses
offers a means of extracting more information from the available muscles than
conventional methods. By identifying repeatable patterns of muscle activity
across multiple muscle sites rather than relying on independent EMG signals it is
possible to provide more natural, reliable control of myoelectric prostheses. The
purposes of this study were to (1) determine if participants can perform
distinctive muscle activation patterns associated with multiple wrist and hand
movements reliably and (2) to show that high density EMG can be applied
individually to determine the electrode location of a clinically acceptable
number of electrodes (maximally eight) to classify multiple wrist and hand
movements reliably in transradial amputees. Eight normally limbed subjects (five
female, three male) and four transradial amputee subjects (two traumatic and
congenital) subjects participated in this study, which examined the
classification accuracies of a pattern recognition control system. It was found
that tasks could be classified with high accuracy (85-98%) with normally limbed
subjects (10-13 tasks) and with amputees (4-6) tasks. In healthy subjects,
reducing the number of electrodes to eight did not affect accuracy significantly
when those electrodes were optimally placed, but did reduce accuracy
significantly when those electrodes were distributed evenly. In the amputee
subjects, reducing the number of electrodes up to 4 did not affect classification
accuracy or the number of tasks with high accuracy, independent of whether those
remaining electrodes were evenly distributed or optimally placed. The findings in
healthy subjects suggest that high density EMG testing is a useful tool to
identify optimal electrode sites for pattern recognition control, but its use in
amputees still has to be proven. Instead of just identifying the electrode sites
where EMG activity is strong, clinicians will be able to choose the electrode
sites that provide the most important information for classification.
CI - Copyright (c) 2012 Elsevier Ltd. All rights reserved.

Langue : ANGLAIS