Improving Control of Dexterous Hand Prostheses Using Adaptive Learning
Abstract
At the time of this writing, the main means of control for polyarticulated self-powered hand prostheses is surface electromyography (sEMG). In the clinical setting, data collected from two electrodes are used to guide the hand movements selecting among a finite number of postures. Machine learning has been applied in the past to the sEMG signal (not in the clinical setting) with interesting results, which provide more insight on how these data...
Paper Details
Title
Improving Control of Dexterous Hand Prostheses Using Adaptive Learning
Published Date
Feb 1, 2013
Volume
29
Issue
1
Pages
207 - 219
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