Predicting lower limb motion intent is vital for controlling exoskeleton robots and prosthetic limbs. Surface electromyography (sEMG) attracts increasing attention in recent years as it enables ahead-of-time prediction of motion intentions before actual movement. However, the estimation performance of human joint trajectory remains a challenging problem due to the inter- and intra-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 muscle activity. This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory. The first strategy is to decouple knee joint angles into motion patterns and amplitudes former exhibit low variability while latter show high variability among individuals. By learning through separate network entities, the model manages to capture both the common and personalized gait features. In the second, muscle principal activation masks are extracted from gait cycles in a prolonged walk. These masks are used to filter out components unrelated to walking from raw sEMG and provide auxiliary guidance to capture more gait-related features. Experimental results indicate that our model could predict knee angles with the average root mean square error (RMSE) of 3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the best performance in relevant literatures that has been reported, with reduced RMSE by at least 9.5%.

翻译：预测下肢运动意图对控制外骨骼机器人和假肢至关重要。近年来，表面肌电图因能在实际运动前提前预测运动意图而日益受到关注。然而，由于受试者间和受试者内部的差异，人体关节轨迹的估计性能仍是一个具有挑战性的问题。前者与个体生理差异（如身高和体重）及偏好行走模式有关，后者主要由不规则且与步态无关的肌肉活动引起。本文提出了一种集成两种步态周期启发学习策略的模型，以缓解预测人体膝关节轨迹的挑战。第一种策略是将膝关节角度解耦为运动模式和幅度，前者在个体间表现出低变异性，后者则表现出高变异性。通过独立的网络实体进行学习，该模型能够同时捕捉共性和个性化步态特征。第二种策略是从长时间行走的步态周期中提取肌肉主激活掩码，这些掩码用于滤除原始表面肌电信号中与行走无关的成分，并提供辅助引导以捕获更多步态相关特征。实验结果表明，我们的模型能以平均均方根误差3.03(0.49)度提前50毫秒预测膝关节角度。据我们所知，这是相关文献中报道的最佳性能，均方根误差降低了至少9.5%。