Abstract

Info

Title:: sEMG pattern recognition based on a novel dual-input convolutional neural network

Author(s):: YANG Dan; KAN Xiu; CAO Le; ZHANG Wenyan; MENG Zhuangzhuang; School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Keywords:: Keywords: dual-arm cooperative motion convolutional neural network discriminative feature surface electromyography signal pattern recognition

Abstract:: Abstract: A novel dual-input convolutional neural network (ND-CNN) model is proposed to solve the problems of large amount of motion information contained in dual-arm cooperative motion, difficult to fully interpret and low recognition rate. According to the characteristics of dual-arm motion, two strategies of data sorting and model input are designed, and then, two feature extraction layers with the same structure and shared parameters are used to extract the features of the signal itself and the discriminative features between the signals. Finally, the two kinds of extracted features are applied to realize the recognition of dual-arm cooperative action. In the self-designed dual-arm experiment, ND-CNN is compared with the other 3 advanced neural networks. The experimental results show that the proposed ND-CNN model is superior to the other network models in recognition accuracy and reliability, and that it can effectively recognize the dual-arm surface electromyography signals.