An Efficient Method for Identifying Lower Limb Behavior Intentions Based on Surface Electromyography

Surface electromyography (sEMG) is widely used for analyzing and controlling lower limb assisted exoskeleton robots. Behavior intention recognition based on sEMG is of great significance for achieving intelligent prosthetic and exoskeleton control. Achieving highly efficient recognition while improving performance has always been a significant challenge. To address this, we propose an sEMG-based method called Enhanced Residual Gate Network (ERGN) for lower-limb behavioral intention recognition. The proposed network combines an attention mechanism and a hard threshold function, while combining the advantages of residual structure, which maps sEMG of multiple acquisition channels to the lower limb motion states. Firstly, continuous wavelet transform (CWT) is used to extract signals features from the collected sEMG data. Then, a hard threshold function serves as the gate function to enhance signals quality, with an attention mechanism incorporated to improve the ERGN’s performance further. Experimental results demonstrate that the proposed ERGN achieves extremely high accuracy and efficiency, with an average recognition accuracy of 98.41% and an average recognition time of only 20 ms-outperforming the state-of-the-art research significantly. Our research provides support for the application of lower limb assisted exoskeleton robots.