The Classification of EMG Signals with Zero Retraining in the Influence of User and Rotation Independence

Abstract

The surface electromyogram (EMG) contains information directly related to muscle contraction and modern classification techniques can obtain near-zero error when identifying various gestures over the forearm. However, good results come at a compromise over the ease of use. Once the EMG classifier trained on a user is changed, the accuracy rate will be greatly reduced. Furthermore, changing the position of the forearm also causes drop in accuracy rate. Acknowledging the limitations of EMG classification, this study aims to investigate the EMG signals based on the gestures, and evaluate if there are any gestures which are inherently robust to these variations. The EMG of forearm gestures have been classified in the combined influence user independence, rotation independence and hand exchange independence. Experiment results on 20 participants indicated that truly independent classification can be achieved for most forearm gestures (up to 100%) in some arm positions. Hand exchange is also not feasible as the study has shown that the data field for both hands are fairly different. Out of the nine gestures under study, only the wrist extension was found to be truly independent of all the influences.