基于人工神经网络的表面肌电信号分类器研究进展

doi:10.16180/j.cnki.issn1007-7820.2021.02.011

Abstract

Abstract:

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

ZHOU Xiaobo,ZOU Renling,LU Xuhua,WANG Haibin,ZHANG Junxiang. Research Progress of Surface Electromyography Signal Classifier Based on Artificial Neural Network[J].Electronic Science and Technology, 2021, 34(2): 62-67.

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References 29

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分类器	动作部位	实验对象	平均准确率
LVQ	下肢	健康学生	96.00%
ELM	手指	健康成人	99.50%
ELM	手臂	健康成人	99.20%
WNN	手臂	健康成人	94.67%
WNN	手臂	截肢患者	85.17%
ANFIS	手臂	健康学生	88.90%
ANFIS	手指	健康学生	72.00%
AlexNet	下肢	数据集	99.55%
AlexNet	手指	健康学生	95.00%
GRNN	手指	健康学生	99.23%

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

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