基于下肢sEMG的疲劳模糊增量熵表征方法研究  

Research on entropy of incremental fuzzy entropy representation model for lower limb fatigue based on sEMG

作　　者：石欣 余可祺 敖钰民 秦鹏杰 张杰毅 Shi Xin;Yu Keqi;Ao Yumin;Qin Pengjie;Zhang Jieyi(School of Automation,Chongqing University,Chongqing 400044,China;Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China;Sichuan Electric Vocation and Technical College,Chengdu 611133,China)

机构地区：[1]重庆大学自动化学院,重庆400044 [2]中国科学院深圳先进技术研究院,深圳518055 [3]四川电力职业技术学院,成都611133

出　　处：《仪器仪表学报》2024年第5期271-280,共10页Chinese Journal of Scientific Instrument

基　　金：国防科技创新特区项目(18-H863-31-ZD-002-002-05);深圳市医学研究专项资金(B2302002)项目资助。

摘　　要：连续运动中,基于表面肌电信号(sEMG)外骨骼机器人与人进行协同运动控制,肌肉产生疲劳将影响人机协同控制的柔顺性及鲁棒性。本文创新性地提出模糊增量熵(EIFEn)用以表征肌肉疲劳程度,并对肌肉疲劳阶段的较为客观划分;采集人体连续抬腿运动中下肢12块肌肉的表面肌电信号,提出基于变异性敏感系数SVR肌肉疲劳敏感度判断方式,实现有效肌肉选取,提出基于均模积的自适应阈值动作切分法,将完整信号切分并提取单个动作信号序列,通过分析计算,对疲劳趋势进行表征。实验结果表明,本文模型相比时域频域算法具有较为明显的肌肉疲劳表征梯度特征,与fApEn及FFDispEn相比具有较好的疲劳表征能力,用于疲劳等级聚类的戴维森堡丁指数(DBI)为0.39,可提高外骨骼人机协同控制,为实现疲劳分阶段补偿助力提供参考。In continuous motion,based on surface electromyography(sEMG)signals,exoskeleton robots and humans collaborate in motion control.Muscle fatigue will affect the flexibility and robustness of human-machine collaborative control.This article innovatively proposes the use of Entropy of Incremental Fuzzy Entropy and constructs a fatigue characterization model,and objectively divides the stages of muscle fatigue;Collect sEMG signals of twelve muscles in the lower limbs during repeated continuous leg lifting movements,propose a method based on the variability sensitivity coefficient SVR to determine muscle fatigue sensitivity,achieve effective muscle selection for this movement,reduce data dimensions,propose an adaptive threshold action segmentation method based on mean squared product,segment the complete signal and extract a single action signal sequence,and analyze and calculate the fatigue trend through this model.The experimental results of the subjects show that the model proposed in this paper has a more obvious gradient feature for muscle fatigue characterization compared to time-domain and frequency-domain algorithms,and has better fatigue characterization ability compared to fApEn and FFDispEn.Davies Bouldin Index for fatigue level clustering is 0.39.This provides a reference for improving the collaborative control of exoskeletons and achieving phased compensation assistance for fatigue.

关 键 词：SEMG 肌肉疲劳 动作切分 模糊增量熵 特征提取 聚类 

分 类 号：TH70[机械工程—仪器科学与技术]