Torque Prediction of Ankle Joint from Surface Electromyographic Using Recurrent Cerebellar Model Neural Network

Abstract

Joint torque prediction plays an important role in quantitative limb rehabilitation training and the exoskeleton robot. The Surface electromyography signal (sEMG) with the advantages of non-invasive and easy collection can be applied to the prediction of human muscle force. By utilizing the sEMG, the recurrent cerebellar model neural network (RCMNN), which has better generalization and computational power than the traditional neural network has been used to predict the joint torque. In this work, a smooth function with adaptive coefficient is employed to polish the results of RCMNN, the proposed method shows great performance on torque prediction with the correlation coefficient between the torque and the estimation result up to 98.43%, such advanced model paves the way to the application on the quantitative rehabilitation training.