Grasping force prediction based on sEMG signals

Ma, Ruyi; Zhang, Leilei; Li, Gongfa; Jiang, Du; Xu, Shuang; Chen, Disi

Abstract

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.

Citation

Ma, R., Zhang, L., Li, G., Jiang, D., Xu, S., & Chen, D. (2020). Grasping force prediction based on sEMG signals. Alexandria Engineering Journal, 59(3), 1135-1147. https://doi.org/10.1016/j.aej.2020.01.007

• Journal Article Type: Article
• Acceptance Date: Jan 3, 2020
• Online Publication Date: Jan 16, 2020
• Publication Date: Jun 1, 2020
• Deposit Date: May 18, 2023
• Publicly Available Date: May 18, 2023
• Journal: Alexandria Engineering Journal
• Print ISSN: 1110-0168
• Electronic ISSN: 2090-2670
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 59
• Issue: 3
• Pages: 1135-1147
• DOI: https://doi.org/10.1016/j.aej.2020.01.007
• Keywords: sEMG; Gene expression program- ming algorithm; Force prediction; Pattern recognition
• Public URL: https://uwe-repository.worktribe.com/output/10535919
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1110016820300089?via%3Dihub