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Predicting lower limb motion intent is vital for controlling exoskeletonrobots and prosthetic limbs. Surface electromyography (sEMG) attractsincreasing attention in recent years as it enables ahead-of-time prediction ofmotion intentions before actual movement. However, the estimation performanceof human joint trajectory remains a challenging problem due to the inter- andintra-subject variations. The former is related to physiological differences(such as height and weight) and preferred walking patterns of individuals,while the latter is mainly caused by irregular and gait-irrelevant muscleactivity. This paper proposes a model integrating two gait cycle-inspiredlearning strategies to mitigate the challenge for predicting human knee jointtrajectory. The first strategy is to decouple knee joint angles into motionpatterns and amplitudes former exhibit low variability while latter show highvariability among individuals. By learning through separate network entities,the model manages to capture both the common and personalized gait features. Inthe second, muscle principal activation masks are extracted from gait cycles ina prolonged walk. These masks are used to filter out components unrelated towalking from raw sEMG and provide auxiliary guidance to capture moregait-related features. Experimental results indicate that our model couldpredict knee angles with the average root mean square error (RMSE) of3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the bestperformance in relevant literatures that has been reported, with reduced RMSEby at least 9.5%.

Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG