A complete process for human dynamics analysis including parameter identification and sEMG-torque estimation

Abstract

In order to analyze the motion of the human lower limb, a comprehensive process was created in this paper. Firstly, an exoskeleton robot platform was leveraged and a dynamic model of human-exoskeleton lower limb was created for identifying human parameters. Then the sEMG signal was utilized to extract information about muscle activity through multi-group experiments, and a backpropagation neural network (BPNN) was built to forecast joint torque. An inverse dynamics analysis combining the human motion data with the dynamic model can not only verify the reliability of prediction result by this BPNN but also the correctness of identified results before. Moreover, the mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation coefficient (PCC) were used as evaluation index of both identification and prediction results. The proposed protocol can give accurate identified parameters for subject and estimated joint torque from sEMG during swing motion. We believe it can be extended to various types of human motion movement and potentially applied to complete human motion analysis.