Estimation of Knee Movement from Surface EMG Using Random Forest with Principal Component Analysis

• DOI: 10.3390/electronics9010043
• Publication Date: 2019-12-30T10:49:41Z
• Authors: Zhong Li; Xiaorong Guan; Kaifan Zou; Cheng Xu
• Keywords: back propagation; sEMG; principal component analysis; 0202 electrical engineering, electronic engineering, information engineering; knee; 02 engineering and technology; random forest; estimation model

To study the relationship between surface electromyography (sEMG) and joint movement, and to provide reliable reference information for the exoskeleton control, the sEMG and the corresponding movement of the knee during the normal walking of adults have been measured. After processing the experimental data, the estimation model for knee movement from sEMG was established using the novel method of random forest with principal component analysis (RFPCA). The influence of the sample size and the previous sEMG data on the prediction efficiency was analyzed. The estimation model was not sensitive to the sample size when samples increased to a certain value, and the results of different previous sEMG showed that the prediction accuracy of the estimation models did not always improve with the increasing features of input. By comparing the estimation model of back propagation neural network with principal component analysis (BPPCA), it was found that RFPCA was suitable for all participants in the experiment with less execution time, and the root mean square error was around 5° which was lower than BPPCA with errors varying from 7° to 25°. Therefore, it was concluded that the RFPCA method for the estimation of knee movement from sEMG is feasible and could be used for motion analysis and the control of exoskeleton.