Instantaneous recognition method for lower limb continuous motion based on onset-window surface electromyography data.

Human-robot collaboration in lower-limb rehabilitation devices imposes stringent requirements on both the recognition accuracy of motion intention and real-time responsiveness. The precise recognition of lower limb motion based on surface electromyography (sEMG) has always been a primary focus of study. However, achieving low-delay recognition in lower limb continuous motion while maintaining accuracy remains a challenge, which is key to unlocking the full potential for the effective deployment and widespread application of robots. An innovative recognition method in lower limb continuous motion was presented in this paper, which investigated the instantaneous recognition network (IRN) and continuous recognition model. The comparative analysis revealed that by utilizing an optimal length of 210 for the onset-window sEMG data, the proposed IRN could substantially reduce the time delay from 300ms/350ms to 60ms at the methodological level. The implementation of the class-balanced method (CBM) enhanced motion recognition accuracy by an additional 4.83% within the onset window. The continuous recognition model was validated across seven scenarios, comprehensively covering all potential situations in daily continuous movements, and achieved an average accuracy of 96.31%. This study demonstrates the potential of the proposed instantaneous recognition method to enhance performance in lower limb continuous motion, providing an innovative approach for research on human-robot synchronization.