Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (2): 62-67.doi: 10.16180/j.cnki.issn1007-7820.2021.02.011

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Research Progress of Surface Electromyography Signal Classifier Based on Artificial Neural Network

ZHOU Xiaobo1,ZOU Renling1,LU Xuhua2,WANG Haibin2,ZHANG Junxiang1   

  1. 1. School of Medical Instrument and Food Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
    2. The Second Military Medical University,Chang Zheng Hospital,Shanghai 200003,China
  • Received:2019-11-11 Online:2021-02-15 Published:2021-01-22
  • Supported by:
    National Natural Science Foundation of China(61803265);National Key R&D Program of China(2018YFC2002601);University of Shanghai for Science and Technology Medical Cross Project(1019308505)


The surface electromyography signal is an important physiological electrical signal. The human body rehabilitation motion recognition system based on the surface electromyography is easy to operate, hurtless to the body and no interference to motion, and has broad application prospects. The limb rehabilitation motion recognition system heavily relies on signal feature extraction and the use of classifiers. In this paper, the surface electromyography signal based on artificial neural network including LVQ classifier, ELM classifier, WNN classifier, ANFIS classifier, Alex Net classifier, and GRNN classifier are reviewed and discussed. After the review and comparison of various classifiers, some shortcomings are found and the future research directions and development trends of the classifiers are analyzed and prospected, which provides a reference for relevant research in the future.

Key words: surface electromyography, pattern recognition, signal processing, classification algorithm, artificial neural network, classifier

CLC Number: 

  • TP18