Robust motion estimation with user-independent sEMG features extracted by correlated components analysis

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.