Real-time myoelectric prosthetic-hand control to reject outlier motion interference using one-class classifier

Electromyography (EMG) has been popularly used as interface command to achieve a natural control for myoelectric prosthetic-hands. Traditional EMG-based recognition methods always only focus on the classification of target motion classes that were defined in the training phase, but have no ability to reject outlier motion interferences that did not present before. In this paper, a hybrid classifier that combines one one-class Gaussian classifiers and a multi-class LDA was constructed to achieve EMG-based motion classification, in which Gaussian classifiers were used to reject outlier interferences, while LDA was used to classify target motion samples. The robust hybrid classifier is easily built and has low run-time complexity. Extensive experiments were conducted to verify the performance of the proposed hybrid classifier, where 91.6% of target motion recognition accuracy and 96.5% of outlier motion rejection accuracy were respectively obtained. Finally, the hybrid classifier was involved to achieve a robust and real-time control of a myoelectric prosthetic-hand.