基于表面肌电图手势动作意图识别的系统综述

doi:10.3969/j.issn.1006-9771.2022.09.005

Abstract

Abstract:

Objective To systematicly review the researches of gesture action intent recognition based on surface electromyography (sEMG).
Methods Experimental researches on gesture action intention recognition based on sEMG were retrieved from CNKI, Wanfang Data, PubMed and Web of Science. The literatures were screened, and the classification methods and other related factors were summarized.
Results A total of 735 researches were returned, and 25 researches were finally included. The publication time was mainly from 2012 to 2021. The subjects were healthy people or amputees. The classification model included traditional machine learning models and deep learning models. Other related factors included acquisition, noise interference and sliding window size.
Conclusion Traditional machine learning models based on sEMG signals have been maturely applied, and gesture recognition with deep learning models are of great potential. The individual differences of subjects, the real-time requirements of gesture classification and the stability requirements of sEMG devices still need to be addressed.

Key words: human-machine interaction, surface electromyography, gesture recognition, machine learning, systematic review

CLC Number:

R496

ZHU Xu,LIU Jing,DONG Zeping,QIU Dawei. Gesture action intent recognition based on surface electromyography: a systematic review[J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2022, 28(9): 1032-1038.

Figures/Tables 2

纳入文献	受试者	手势数	分类模型	结论
Wu等^[12](2020)	4例男性、2例女性	6	BPNN	与sEMG特征数量相比,测量位置数量变化对识别结果影响更大
Naik等^[13](2016)	5例截肢受试者	12	ICA-SSO	减少肌电传感器的数量,提高肌电控制系统的鲁棒性
Du等^[14](2017)	23例健康受试者	22	DBN	先进的分类器使用高密度sEMG数据集比稀疏多通道sEMG得到更高准确率
Chen等^[15](2020)	18例健康受试者	8	3D-CNN	3D-CNN比2D-CNN能更好识别高密度sEMG数据集,但处理时间也增加
Duan等^[16](2018)	4例男性、4例女性	9	LDA	使用3个传感器识别9个手势,平均准确率91.7%
Zhang等^[17](2019)	8例男性、4例女性	5	ANN	手势识别率98.7%
Saeed等^[18](2021)	10例健康受试者、6例截肢受试者	11	LDA、ANN	与原始信号相比,特征组合作为输入能得到更高识别率
Tang等^[19](2012)	6例男性	11	LDA、KNN、NB	通过定义新的特征和设计新的级联结构提高手势识别率
Xue等^[20](2021)	8例男性、2例女性	13	CCA-OT	减少不同受试者肢体之间概率函数漂移的必要性
Sun等^[21](2019)	8例男性、2例女性	52	GFM	GFM分解特征因子对提高肌电手势分类准确率有积极作用
Cao等^[22](2021)	36例健康受试者	4	AMPSO-SVM	遗传算法用于解决高维和冗余问题
Junior等^[23](2020)	8例男性、5例女性	6	ELM、KNN、SVM-RBF等	降维和特征量缩减可以有效提高手势识别率
Jie等^[24](2021)	7例男性、1例女性	5	PSO	少量特征仍然可以达到高密度sEMG信号的手势识别率
Sharif等^[28](2020)	6例男性、2例女性	16	SVM、CNN	动态手势的手势识别精度不如静态手势
Zhai等^[29](2017)	40例健康受试者、11例截肢受试者	49	CNN	重新校准后的CNN可以补偿sEMG基准漂移
Cheng等^[30](2021)	20例男性、7例女性	52	CNN	sEMG多特征图像能够更好实现信息互补,提高手势分类准确率
Asif等^[31](2020)	18例健康受试者	10	CNN	单个网络会明显偏好个别手势
许留凯等^[32](2021)	40例健康受试者	49	CNN	CNN结合能量核相图有着较好的手势识别精度
李沿宏等^[33](2021)	20例男性、7例女性	52	多流卷积网络	多流卷积网络学习sEMG信号的时序信息
Nasri等^[34](2019)	35例健康受试者	6	GRU	GRU实时评估系统能区分大约80%训练手势
Zhang等^[35](2020)	8例男性、5例女性	21	GRU	文献提出的模型有着更好的实时性能和准确性
Yu等^[36](2019)	8例男性	150	DBN	特征级融合作为网络的输入有着最好的手势识别率

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Gesture action intent recognition based on surface electromyography: a systematic review

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