[论文解读] Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG

Xueming Fu, Hao Zheng+10 more|arXiv (Cornell University)|Jul 25, 2023

Muscle activation and electromyography studiesEngineering被引用 3

一句话总结

本文提出一种受步态周期启发的学习策略，以提升从表面肌电图（sEMG）连续预测膝关节轨迹的性能。通过将关节角度分解为运动模式与振幅，并利用长时间行走获得的肌肉主激活掩码，该模型在50ms预测提前量下实现了3.03°的平均RMSE，相较先前工作降低了9.5%。

ABSTRACT

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%.

研究动机与目标

• 解决基于sEMG的膝关节轨迹预测中存在的人与人之间及个体内部的变异性问题。
• 提升下肢运动意图的连续、实时预测性能，以用于外骨骼和假肢控制。
• 通过利用步态周期结构与肌肉激活模式，降低预测误差。
• 将关节角度特征解耦为低变异性运动模式与高变异性振幅，以提升泛化能力。

提出的方法

• 将膝关节角度分解为运动模式（人与人之间变异性较低）与振幅（变异性较高），分别进行学习。
• 训练专用神经网络分别学习运动模式与振幅，以捕捉共有的与个性化的步态特征。
• 从延长的步态周期中提取肌肉主激活掩码，以过滤非步态相关的sEMG成分。
• 将掩码用作辅助指导，增强sEMG编码器中与步态相关特征的学习。
• 整合解耦的预测结果，以重建完整的膝关节角度轨迹。
• 采用最小化预测与实际关节角度间RMSE的损失函数，对模型进行端到端优化。

实验结果

研究问题

• RQ1 将膝关节角度解耦为运动模式与振幅是否能提升基于sEMG的轨迹预测精度？
• RQ2 基于步态周期提取的肌肉激活掩码在过滤非步态相关sEMG噪声方面效果如何？
• RQ3 所提出的学習策略在多大程度上减少了sEMG预测中的人与人之间及个体内部的变异性？
• RQ4 该模型能否实现显著提前于实际运动的实时、连续预测？
• RQ5 与最先进方法相比，该模型在RMSE与预测延迟方面表现如何？

主要发现

• 该模型在预测膝关节轨迹时实现了3.03°（±0.49°）的平均均方根误差（RMSE）。
• 该模型可提前50ms预测膝关节角度，从而实现在辅助设备中实现早期干预。
• 与最佳性能的先前方法相比，该方法将RMSE降低了至少9.5%。
• 运动模式与振幅的解耦显著提升了在具有不同生理特征受试者间的泛化能力。
• 采用受步态周期启发的肌肉激活掩码，通过过滤非步态相关肌肉活动，增强了特征学习。
• 该模型在长时间行走数据上表现出稳健性能，证实了基于步态周期的特征提取具有稳定性。