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Volume 8 Issue 3
Mar.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(3): 512-533
Dezhen Xiong, Daohui Zhang, Xingang Zhao and Yiwen Zhao, "Deep Learning for EMG-based Human-Machine Interaction: A Review," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 512-533, Mar. 2021. doi: 10.1109/JAS.2021.1003865
Citation: Dezhen Xiong, Daohui Zhang, Xingang Zhao and Yiwen Zhao, "Deep Learning for EMG-based Human-Machine Interaction: A Review," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 512-533, Mar. 2021. doi: 10.1109/JAS.2021.1003865
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Deep Learning for EMG-based Human-Machine Interaction: A Review

doi: 10.1109/JAS.2021.1003865
Funds:  This work was supported in part by the National Natural Science Foundation of China (U1813214, 61773369, 61903360), the Self-planned Project of the State Key Laboratory of Robotics (2020-Z12), and China Postdoctoral Science Foundation funded project (2019M661155)
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    Abstract
  • 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.

     

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    Highlights

    • This paper attempts to provide a literature review of deep learning in EMG pattern recognition tasks for human-machine interaction. We want to offer a comprehensive analysis of current research, which is the first time to the best of we can know.
    • This work will discuss traditional topics like movement classification, joint angle prediction, and force/torque estimation and the latest issues like inter-session/subject, robustness under non-ideal conditions, multimodal sensors fusion, and the applications in physical systems.
    • The advantages, challenges, and opportunities to solve EMG recognition questions through deep learning will be analyzed. Moreover, four future directions that we believe are important for future development will be covered to pave the way for future research.

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