基于VMD-ELMAN的肌电信号对下肢关节角度预测  

Prediction of Lower Limb Joint Angle Based on VMD-ELMAN Electromyographic Signals

作　　者：汪文淼 WANG Wenmiao(Virginia Tech and State University School of Engineering,Blackburg,Virginia 24061,USA)

机构地区：[1]弗吉尼亚理工学院暨州立大学,弗吉尼亚州黑堡镇24061

出　　处：《计算机科学》2024年第3期257-264,共8页Computer Science

摘　　要：表面肌电信号(Surface Electromyography,sEMG)提前于人体动作产生,常用于预测人体行为运动意图。但由于其自身的非平稳性与时变特性,因此难以较为准确地预测人体下肢关节角度变化。文中研究人体下肢肌肉针对正常行走、上下楼梯这3种动作进行的肌肉选取,提出了一种VMD-ELMAN角度拟合算法,提高了表面肌电信号角度预测精度,增强了角度预测的实时性,为提升人与外骨骼设备人机融合度提供了有效的解决方案。实验结果表明,相比常见角度拟合算法,所提算法的时间耗时较短,在3种常见动作中,髋关节角度预测值RMSE的最高精度达0.5789,膝关节角度预测值RMSE均在0.2以内,预测精度均优于常见模型,模型鲁棒性强。Surface electromyography(sEMG)signals are generated in advance of human movements and are commonly used to predict human behavior and motor intentions.However,due to its inherent non-stationary and time-varying characteristics,it is difficult to accurately predict changes in the angle of human lower limb.This paper presents a VMD-ELMAN angle fitting algorithm for muscle selection of human lower limb muscles for three movements:normal walking,ascending stairs and descending stairs.This algorithm improves the accuracy of surface electromyography signal angle prediction,enhances the real-time perfor-mance of angle prediction,and provides an effective solution for improving human-machine integration with exoskeleton devices.The Experimental results show that compared to common angle fitting algorithms,the proposed algorithm is less time-consuming.Among the three common movements,the highest accuracy of the hip joint angle prediction value RMSE is 0.5789,and the knee joint angle prediction value RMSE is within 0.2.Its prediction accuracy is superior to common models,and the model has strong robustness.

关 键 词：人机融合 SEMG 特征提取 角度预测 ELMAN神经网络 

分 类 号：TN911.72[电子电信—通信与信息系统]