基于sEMG的下肢连续运动切换态实时识别方法  

Real-time recognition method of switching states of continuouslower limb movements based on sEMG

作　　者：石欣 敖钰民 范智瑞 余可祺 秦鹏杰 Shi Xin;Ao Yumin;Fan Zhirui;Yu Keqi;Qin Pengjie(School of Automation,Chongqing University,Chongqing 400044,China;Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,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

出　　处：《仪器仪表学报》2024年第4期165-174,共10页Chinese Journal of Scientific Instrument

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

摘　　要：在外骨骼与人进行自然人机交互(HRI)过程中,准确快速地识别下肢连续运动中的切换态至关重要。切换态sEMG信号即包含切换前后运动信息,又包含切换的瞬态信息,难以直接用于识别。为了快速准确地识别切换态,本文提出了FMICMD-LACNN的实时识别方法。提出了自适应多分量瞬时频率估计方法来提升多元本征线性调频模态分解(MICMD)计算效率,提出了分量能量惩罚因子提高MICMD分解精度,从而形成了快速多元本征调频模态分解(FMICMD)算法。针对FMICMD分解后sEMG信号,构建了LACNN识别模型,实现了快速且准确的切换态识别。本研究采集了10名受试者8种常见下肢连续运动切换态下的sEMG信号进行实验验证。结果表明,对于这8种切换态,该方法平均识别准确率为98.35%,平均识别时间仅约8 ms,均优于CNN-LSTM、E2CNN以及CNN-BiLSTM方法。该方法具有较高的准确率和实时性,能够满足外骨骼与人体快速自然交互的需求。Accurately and rapidly identifying the switching states in continuous lower limb movements is crucial for natural human-robot interaction(HRI)with exoskeletons.The switching state sEMG signals contain both pre-and post-switching movement information,as well as transient information related to the switching,making them difficult to directly use for recognition.In order to quickly and accurately identify the switching states,this paper proposes a real-time recognition method called FMICMD-LACNN.An adaptive multi-component instantaneous frequency estimation method is proposed to improve the computational efficiency of the multivariate intrinsic chirp mode decomposition(MICMD),and a component energy penalty factor is proposed to enhance the decomposition accuracy of MICMD,thus forming the fast multivariate intrinsic chirp mode decomposition(FMICMD)algorithm.For the sEMG signals decomposed by FMICMD,a LACNN recognition model was established to achieve fast and accurate switching states identification.This study collected sEMG signals from 10 subjects in 8 common lower limb continuous motion switching states for experimental verification.The results show that for these 8 switching states,the average recognition accuracy of this method is 98.35%,and the average recognition time is only about 8 ms,which is better than the CNN-LSTM,E2CNN and CNN-BiLSTM methods.This method has high accuracy and real-time performance,and can meet the needs of fast and natural interaction between the exoskeleton and the human body.

关 键 词：运动意图识别 表面肌电信号 MICMD CNN 

分 类 号：TH70[机械工程—仪器科学与技术]