Robust motion estimation with user-independent sEMG features extracted by correlated components analysis. (January 2023)

• Record Type: Journal Article
• Authors: Zhang, Song; Lin, Jianeng; You, Yugen; Yu, Ningbo; Han, Jianda
• Is Part Of: Measurement and control. Volume 56:Number 1/2(2023)
• Page Start: 432
• Page End: 441
• Publication Date: 2023-01
• Subjects: Surface electromyogram (sEMG) -- motion estimation -- user-independent features -- correlated components analysis (CorrCA); Automatic control -- Periodicals; Engineering instruments -- Periodicals; Production engineering -- Periodicals; 629.8
• DOI: 10.1177/00202940221105092
• Languages: English
• ISSNs: 0020-2940

Abstract

Motion estimation from surface electromyogram (sEMG) signals has been studied extensively over the past decades. Nevertheless, it is challenging for novel subjects to adapt to a trained estimation model since sEMG signals inherently contain user-dependent features that interfere with the estimation model and reduce the estimation accuracy. To achieve accurate motion estimation, a strategy of correlated components analysis-based random forest regressor (CorrCA-RFG) was proposed. The proposed CorrCA-RFG firstly uses CorrCA to extract user-independent features related to motion among multiple subjects, and obtain the projection vectors from sEMG data to the motion-dependent feature space. Then, the RFG is trained by the user-independent sEMG features and establishes the estimation model. To validate the effectiveness of the proposed CorrCA-RFG, this strategy was tested on a public dataset and an experimental study and compared to three methods, namely random forest regressor (RFG), canonical components analysis-based random forest regressor (CCA-RFG), and a convolutional neural network (CNN). For both cases, the estimation performance of the CorrCA-RFG outperformed the other three methods. These results demonstrate that the proposed CorrCA-RFG enables robust motion estimation by extracting user-independent sEMG features.