Lower Limb Motion Recognition Based on Surface Electromyography Decoding Using S-Transform Energy Concentration

Abstract

Lower limb motion recognition using surface electromyography (EMG) enhances human-computer interaction for intelligent prostheses. This study proposes a surface electromyography (EMG)-based scheme for lower limb motion recognition to enhance human-computer interaction in intelligent prostheses. Addressing the loss of phase information in existing methods, the approach combines S-transform energy concentration and multi-channel fusion analysis. EMG signals from six lower limb muscles of 10 subjects performing four movements (level walk, stair ascent, stair descent, and obstacle crossing) were analyzed. Correlation analysis identified the most relevant and least correlated muscles, optimizing signal quality. Using support vector machines (SVM), motion recognition accuracy was evaluated for single-channel and multi-channel signals. Results indicated that the semi-tendon and rectus femoris muscles achieved 80.71% accuracy with simple time-frequency features, while the medial gastrocnemius and rectus femoris reached 93.70% accuracy with S-transform energy concentration. Multi-channel fusion (rectus femoris, biceps femoris, and medial gastrocnemius) based on S-transform achieved over 96% accuracy, demonstrating superior recognition performance and potential for improving adaptive human-robot interaction in prosthetic control.