Fluctuating emg signals: investigating long-term effects of pattern matching algorithms.

In this paper, we investigate the behavior of state-of-the-art pattern matching algorithms when applied to electromyographic data recorded during 21 days. To this end, we compare the five classification techniques k-nearest-neighbor, linear discriminant analysis, decision trees, artificial neural networks and support vector machines. We provide all classifiers with features extracted from electromyographic signals taken from forearm muscle contractions, and try to recognize ten different hand movements. The major result of our investigation is that the classification accuracy of initially trained pattern matching algorithms might degrade on subsequent data indicating variations in the electromyographic signals over time.