Towards semi-supervised myoelectric finger motion recognition based on spatial motor units activation  

作　　者：GUO WeiChao WANG Mian SHENG XinJun ZHU XiangYang 

机构地区：[1]State Key Laboratory of Mechanical System and Vibration,School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China [2]Meta Robotics Institute,Shanghai Jiao Tong University,Shanghai 200240,China

出　　处：《Science China(Technological Sciences)》2022年第6期1232-1242,共11页中国科学（技术科学英文版）

基　　金：supported in part by the China National Key R&D Program(Grant No.2018YFB1307200);the National Natural Science Foundation of China (Grant Nos.51905339&91948302)。

摘　　要：It is vital to recognize the intention of finger motions for human-machine interaction(HMI).The latest research focuses on fine myoelectric control through the decoding of neural motor unit action potential trains(MUAPt) from high-density surface electromyographic(sEMG) signals.However,the existing EMG decoding algorithms rarely obtain the spatial matching relationship between decoded motion units(MU) and designated muscles,and the control interface can only recognize the trained hand gestures.In this study,a semi-supervised HMI based on MU-muscle matching(MMM) is proposed to recognize individual finger motions and even the untrained combined multi-finger actions.Through automatic channel selection from high-density s EMG signals,the optimal spatial positions to monitor the MU activation of finger muscles are determined.Finger tapping experiment is carried out on ten subjects,and the experimental results show that the proposed s EMG decomposition algorithm based on MMM can accurately identify single finger motions with an accuracy of 93.1%±1.4%,which is comparable to that of state-of-the-art pattern recognition methods.Furthermore,the MMM allows unsupervised recognizing the untrained combined multi-finger motions with an accuracy of 73%±3.8%.The outcomes of this study benefit the practical applications of HMI,such as controlling prosthetic hand and virtual keyboard.

关 键 词：myoelectric control SEMI-SUPERVISED motor units activation channel selection template matching 

分 类 号：R318[医药卫生—生物医学工程] TN911.7[医药卫生—基础医学]