2009 | OriginalPaper | Chapter
Dynamic Behavior of Time-Domain Features for Prosthesis Control
Authors : Stefan Herrmann, Klaus J. Buchenrieder
Published in: Computer Aided Systems Theory - EUROCAST 2009
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Myoelectric hand-prostheses are used by patients with either above- or below-elbow amputations and actuated with a minimal microvolt-threshold myoelectric signal (MES). Prehensile motions or patterns are deduced from the MES by classification. Current approaches act on the assumption, that MES is adiabatic-invariant and unaffected by fatigue of contributory muscles. However, classifiers fail on the onset of muscle fatigue and cannot distinguish between voluntary-, submaximal-contraction and an intentional release of muscle tension. As a result, patients experience a gradual loss of control over their prostheses. In this contribution we show, that the probability distributions of extracted time- and frequency-domain features are fatigue dependent with regard to locality, skewness and time. Also, we examine over which time-frame, established classifiers provide unambiguous results and how classifiers can be improved by the selection of a proper sampling-window size and an appropriate threshold for select features.