摘要
Electromyography(EMG)has already been broadly used in human-machine interaction(HMI)applications.Determining how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgently need a solution.Recently,many EMG pattern recognition tasks have been addressed using deep learning methods.In this paper,we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based HMI.An overview of typical network structures and processing schemes will be provided.Recent progress in typical tasks such as movement classification,joint angle prediction,and force/torque estimation will be introduced.New issues,including multimodal sensing,inter-subject/inter-session,and robustness toward disturbances will be discussed.We attempt to provide a comprehensive analysis of current research by discussing the advantages,challenges,and opportunities brought by deep learning.We hope that deep learning can aid in eliminating factors that hinder the development of EMG-based HMI systems.Furthermore,possible future directions will be presented to pave the way for future research.
基金
supported in part by the National Natural Science Foundation of China(U1813214
61773369
61903360)
the Selfplanned Project of the State Key Laboratory of Robotics(2020-Z12)
China Postdoctoral Science Foundation funded project(2019M661155)。
作者简介
Dezhen Xiong received the B.E.degree in automation from North University of China,in 2018.He is currently pursuing the Ph.D.degree with the State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences.He is also currently with the University of Chinese Academy of Sciences.His research interests include biomedical signal processing,blind source separation,pattern recognition,and deep learning,e-mail:xiongdezhen@sia.cn;Corresponding author:Daohui Zhang(M’19)received the B.E.degree in mechanical engineering and automation from Northeastern University,in 2010,and the Ph.D.degree in pattern recognition and intelligent system from Shenyang Institute of Automation,Chinese Academy of Sciences,in 2018.He is currently an Associate Professor with the State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences.His research interests include nonlinear estimation and control,robotics,and pattern recognition,e-mail:zhangdaohui@sia.cn;Corresponding author:Xingang Zhao(M’12)received the B.E.and M.E.degrees in mechanics from Jilin University,in 2000 and 2004,respectively,and the Ph.D.degree in pattern recognition and intelligent systems from Shenyang Institute of Automation,Chinese Academy of Sciences,in 2008.From 2015 to 2016,he was a Visiting Scientist at the Rehabilitation Institute of Chicago,Chicago,USA.He is currently a Professor at Shenyang Institute of Automation,Chinese Academy of Sciences.His research interests include medical robots,rehabilitation robots,robot control,and pattern recognition,e-mail:zhaoxingang@sia.cn;Yiwen Zhao received the B.Sc.degree in control science and engineering and the M.Sc.degree in mechanical and electrical engineering from Harbin Institute of Technology,in 1995 and 1997,respectively,and the Ph.D.degree in mechanical and electrical engineering from Shenyang Institute of Automation,Chinese Academy of Science in 2000.Since 2000,he has been with the State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,where he is currently a Professor.His research interests include medical robots,autonomous mobile robots,and intelligent system control,e-mail:zhaoyw@sia.cn。
