Interface Operation of Five-finger Combined Movement with Recurrent NACGMN

Abstract

Various man-machine interfaces controlled by electromyogram (EMG) signals such as the myoelectric prosthetic hand have been proposed. General classifiers can not consider unintended motions in the training phase and require learning all the motions. In this paper, the authors propose a motion estimation system with unlearned classes and combined motions based on a muscle synergy model. The proposed method can identify unlearned five-finger combined motions by learning a single motion only. Furthermore, this method utilizes the history of muscle synergy and unlearned motion detection, using a state transition model. In the experiments, it was shown that the discrimination accuracy was sufficient for simple combined motions.