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

<i>Objective</i>. 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.<i>Approach</i>. An innovative recognition method in lower limb continuous motion was presented in this paper, which investigated the instantaneous recognition network (IRN) and continuous recognition (CR) model.<i>Main results.</i>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 300 ms/350 ms to 60 ms at the methodological level. The implementation of the class-balanced method enhanced motion recognition accuracy by an additional 4.83% within the onset window. The CR model was validated across seven scenarios, comprehensively covering all potential situations in daily continuous movements, and achieved an average accuracy of 96.31%.<i>Significance.</i>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.