Classification of finger gestures from myoelectric signals

Abstract

Electromyographic signals may provide an important new class of user interface for consumer electronics. In order to make such interfaces effective, it will be crucial to map EMG signals to user gestures in real time. The mapping from signals to gestures will vary from user to user, so it must be acquired adaptively. In this thesis, I describe and compare three methods for static classification of EMG signals. I then go on to explore methods for adapting the classifiers over time and for sequential analysis of the gesture stream by combining the static classification algorithm with a hidden Markov model. I conclude with an evaluation of the combined model on an unsegmented stream of gestures.