Learn the Temporal-Spatial Feature of sEMG via Dual-Flow Network

Abstract

Surface electromyography (sEMG) signals have been widely used in human–machine interaction, providing more nature control expedience for external devices. However, due to the instability of sEMG, it is hard to extract consistent sEMG patterns for motion recognition. This paper proposes a dual-flow network to extract the temporal-spatial feature of sEMG for gesture recognition. The proposed network model uses convolutional neural network (CNN) and long short-term memory methods (LSTM) to, respectively, extract the spatial feature and temporal feature of sEMG, simultaneously. These features extracted by CNN and LSTM are merged into temporal-spatial feature to form an end-to-end network. A dataset was constructed for testing the performance of the network. In this database, the average recognition accuracy by using our dual-flow model reached 78.31%, which was improved by 6.69% compared to the baseline CNN (71.67%). In addition, NinaPro DB1 is also used to evaluate the proposed methods, receiving 1.86% higher recognition accuracy than the baseline CNN classifier. It is believed that the proposed dual-flow network owns the merit in extracting stable sEMG feature for gesture recognition, and can be further applied into practical applications.