Label Rectified and Graph Adaptive Semi-Supervised Regression for Electrode Shifted Gesture Recognition

Surface electromyography (sEMG) noninvasively records muscle activities. It provides valuable information about muscle contractions and enables real-time decoding into hand gestures. Recently many studies have successfully demonstrated this capability. However, the accuracy of gesture recognition decreases significantly due to electrode shifts. Without increasing the density of electrodes which may cause the curse of dimensionality and result in higher costs, we propose a label rectified and graph adaptive semi-supervised regression (LRGASR) model for electrode shifted gesture recognition. LRGASR on one hand learns an optimal graph to characterize the underlying semantic connectionship of both non-shifted and shifted sEMG samples and takes advantage of label rectification to reduce the feature-label inconsistency of shifted ones. Experimental results show that LRGASR achieved the average recognition accuracies 78.20% and 87.28% on the SeNic and ISRMyo sEMG data sets, which outperforms six existing models.