歩行周期に着想を得た学習戦略によるsEMGからの膝関節軌道の連続予測
Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG
歩行周期を模した2つの学習戦略を組み合わせ、表面筋電図から膝関節角度を50ms先まで予測するモデルを提案し、平均RMSE 3.03度を達成した。
著者: Xueming Fu, Hao Zheng, Luyan Liu, Wenjuan Zhong, Haowen Liu, Wenxuan Xiong, Yuyang Zhang, Yifeng Chen, Dong Wei, Mingjie Dong, Yefeng Zheng, Mingming Zhang
分類: cs.RO, cs.AI
原文アブストラクト
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%.
