Improved Long Short-term Memory Network for Gesture Recognition

Abstract

Surface EMG contains a lot of physiological information reflecting the intention of human movement. Gesture recognition by surface EMG has been widely concerned in the field of human-computer interaction and rehabilitation. At present, most studies on gesture recognition based on surface EMG signal are obtained by discrete separation method, ignoring continuous natural motion. A gesture recognition method of surface EMG based on improved long short-term memory network is proposed. sEMG sensors are rationally arranged according to physiological structure and muscle function. In this paper, the finger curvature is used to describe the gesture state, and the gesture at every moment can be represented by the set of different finger curvature, so as to realize continuous gesture recognition. Finally, the proposed gesture recognition model is tested on Ninapro (a large gesture recognition database). The results show that the proposed method can effectively improve the representation mining ability of surface EMG signal, and provide reference for deep learning modeling of human gesture recognition.