Abstract
Surface electromyographic (SEMG) signals are widely used as inputs for the controllers of upper limb prostheses. The purpose of this paper is to assess several timedomain features used as inputs for two classifiers implemented using linear and quadratic discriminant analysis. Four movements of the forearm were classified using several combinations of features for each type of classifier. The best recognition rate of 95% was obtained for a linear discriminant analysis classifier.
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