基于sEMG信号的关节力矩NARX预测模型

NARX prediction model of joint torque based on sEMG signal

为解决利用力矩传感器控制肌力训练设备所带来的滞后性,利用表面肌电信号(sEMG)超前于运动的特性,设计了基于一组拮抗肌表面肌电信号的关节力矩预测模型。首先搭建康复训练设备为信号采集和实验验证提供条件。将sEMG经过预处理,选择sEMG信号的方差特征作为神经网络输入,利用带有外部输入的非线性自回归(NARX)模型的动态循环神经网络,分别建立了基于关节力矩实际值的超前多步(MSA)预测模型和基于模型预测输出(MPO)的预测模型,通过等张和等长测试实验,比较了MSA和MPO模型的力矩预测性能。实验结果表明,两种模型输出预测值和实际值之间都有极强关联性(皮尔逊相关系数均大于0.95)。随着超前预测的步数增加,MSA模型的预测精度降低,但是超前预测的时间增大。在等张和等长测试中,当超前步数分别小于29和35时,MSA预测精度显著高于MPO(p<0.05),但MPO模型在成本和体积上更具优势。综上所述,两种模型均可以准确预测关节力矩,在实际康复训练设备控制中,可根据应用需求选择不同的力矩预测模型。

To solve the hysteresis caused by using torque sensors to control muscle force training equipment,a joint torque prediction model based on a group of antagonistic surface electromyography(sEMG)is designed in this article.Firstly,the rehabilitation training equipment is built to provide conditions for signal acquisition and experimental verification.sEMG is preprocessed and the variance characteristic of sEMG signal is selected as the neural network input.In addition,a dynamic recurrent neural network with the nonlinear auto-regressive model with exogenous inputs(NARX)is used in this study.A multi-step ahead prediction model(MSA)based on the actual values of joint moments and another model based on model prediction output(MPO)are developed respectively.The torque prediction performance of MSA and MPO models is compared by isotonic and isometric test experiments.Experimental results show that there is a strong correlation between the predicted output value and the actual output value of the two models(Pearson correlation coefficient is greater than 0.95).As the number of advance prediction steps increases,the prediction accuracy of MSA model decreases.However,the advance prediction time increases.When n is less than 29 and 35,the prediction accuracy of MSA is significantly higher than that of MPO(p<0.05).But the MPO model has advantages in cost and size.In summary,two models proposed in this article can accurately predict joint torques.In actual rehabilitation training equipment control,different torque prediction models can be selected according to application requirements.