Grasping force prediction based on sEMG signals

Alexandria Engineering Journal

卷:59

Leilei Zhang¹  Du Jiang¹  Disi Chen²  Shuang Xu³  Gongfa Li⁴  Ruyi Ma⁴ 

[1] Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan 430081, China;

[2] Research Center of Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan 430081, China;

[3] Key Laboratory of Metallurgical Equipment and Control Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China;

关键词: sEMG;    Gene expression programming algorithm;    Force prediction;    Pattern recognition;   

DOI  :  

来源: DOAJ

【 摘 要 】

In order to realize the force control, when the prosthetic hand grasps the object, the forearm electromyography signal is collected by the multi-channel surface electromyography (sEMG) acquisition system. The grasping force information of the human hand is recorded by the six-dimensional force sensor. The root mean square (RMS) of the electromyography signal steady state is selected, which is an effective feature. The gene expression programming algorithm (GEP) and BP neural network are used to construct the prediction model and predict the grasping force. The force prediction accuracy of GEP algorithm and BP neural network algorithm are discussed under different grasping power levels and different grasping modes. The performance of the two algorithm models are evaluated by two measures of root mean square error (RMSE) and correlation coefficient (CC). The results show that the RMS eigenvalue extracted from the sEMG signal can better characterize the grasping force. The prediction model with GEP algorithm has smaller relative error and higher prediction effect.

【 授权许可】

Unknown