Assessing the progression of muscle fatigue for daily exercises provides vital indicators for precise rehabilitation, personalized training dose, especially under the context of Metaverse. Assessing fatigue of multi-muscle coordination-involved daily exercises requires the neuromuscular features that represent the fatigue-induced characteristics of spatiotemporal adaptions of multiple muscles and the estimator that captures the time-evolving progression of fatigue. In this paper, we propose to depict fatigue by the features of muscle compensation and spinal module activation changes and estimate continuous fatigue by a physiological rationale model. First, we extract muscle synergy fractionation and the variance of spinal module spikings as features inspired by the prior of fatigue-induced neuromuscular adaptations. Second, we treat the features as observations and develop a Bayesian Gaussian process to capture the time-evolving progression. Third, we solve the issue of lacking supervision information by mathematically formulating the time-evolving characteristics of fatigue as the loss function. Finally, we adapt the metrics that follow the physiological principles of fatigue to quantitatively evaluate the performance. Our extensive experiments present a 0.99 similarity between days, a over 0.7 similarity with other views of fatigue and a nearly 1 weak monotonicity, which outperform other methods. This study would aim the objective assessment of muscle fatigue.

翻译：评估日常运动中肌肉疲劳的进展，为精准康复、个性化训练剂量（尤其在元宇宙背景下）提供了关键指标。评估涉及多肌群协调的日常运动中的肌肉疲劳，需要能够表征多肌群时空适应疲劳诱发特征的神经肌肉特征，以及能够捕捉疲劳时域演变进程的估计器。本文提出利用肌肉代偿与脊髓模块激活变化特征描述疲劳，并通过生理学机制模型估计连续疲劳。首先，受疲劳诱发神经肌肉适应性先验启发，我们提取肌肉协同分馏和脊髓模块发放方差作为特征。其次，将特征视为观测值，开发贝叶斯高斯过程以捕捉时域演变进程。第三，通过将疲劳的时域演变特性数学形式化为损失函数，解决监督信息缺失问题。最后，采用符合疲劳生理学原理的指标定量评估性能。广泛实验表明，该方法在日期间相似度达0.99、与其他疲劳视角的相似度超过0.7、弱单调性接近1，均优于其他方法。本研究旨在实现肌肉疲劳的客观评估。