基于肌电信号和极限学习机的下肢关节运动预测  

Prediction of Lower Limb Joint Motion Based on Surface EMG Signal and Extreme Learning Machine

作　　者：石永杰 高学山 罗定吉[3] 吕佳乐 吕鹏飞 刘欢 车红娟[1] 赵鹏 牛军道 郝亮超 Shi Yongjie;Gao Xueshan;Luo Dingji;LYU Jiale;LYU Pengfei;Liu Huan;Che Hongjuan;Zhao Peng;Niu Jundao;Hao Liangchao(School of Electrical and Information Engineering,Guangxi University of Science and Technology,Liuzhou 545000,China;School of Mechatronical Engineering,Beijing Institute of Technology,Beijing 100081,China;School of Mechanical and Transportation Engineering,Guangxi University of Science and Technology,Liuzhou 545000,China)

机构地区：[1]广西科技大学电气与信息工程学院,广西柳州545000 [2]北京理工大学机电学院,北京100081 [3]广西科技大学机械与交通工程学院,广西柳州545000

出　　处：《兵工自动化》2022年第2期87-91,96,共6页Ordnance Industry Automation

基　　金：中国老年失能预防与干预管理网络及技术研究(2020YFC2008503)。

摘　　要：为判断下肢障碍患者的运动意图,通过外骨骼进行康复训练,分析表面肌电信号与下肢关节运动的关系。提取表面肌电信号的均方根(root mean square,RMS)、绝对值均值(mean absolute value,MAV)、波形长度(waveform length,WL)和方差(variance,VAR)作为特征输入信号,采用极限学习机(extreme learning machine,ELM)建立表面肌电信号与下肢关节角度之间的映射关系;对输出结果进行优化滤波以降低模型的误差,实现对下肢膝关节角度连续变化的预测。与传统的反向传播(back propagation,BP)神经网络、径向基神经网络预测结果进行对比,结果证明:极限学习机在通过表面肌电信号预测下肢关节角度变化中有更高的精度。In order to judge the motor intention of patients with lower limb disorders, the relationship between surface electromyography(SEMG) signal and lower limb joint movement was analyzed through rehabilitation training of exoskeleton. The root mean square(RMS), mean absolute value(MAV), waveform length(WL) and variance(VAR) of SEMG signal are extracted as feature input signals, and the mapping relationship between SEMG signal and lower limb joint angle is established by using extreme learning machine(ELM);the output results are optimized and filtered to reduce the error of the model, and the continuous change of lower limb knee angle is predicted. Compared with the traditional back-propagation neural network and radial basis function neural network, the results show that the extreme learning machine has higher accuracy in predicting the change of lower limb joint angle through SEMG signal.

关 键 词：表面肌电信号 极限学习机 多特征提取 关节角度 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]