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:评估日常锻炼的肌肉疲劳程度为精确康复、个性化训练剂量提供关键指标,尤其在元宇宙的背景下考虑。评估多肌肉协调涉及的日常锻炼的疲劳需要表示肌肉补偿和脊髓模块活化改变的神经肌肉特征,并通过一种生理学合理的模型估计连续的疲劳。首先,我们提取了肌肉协同作用的特征分数和脊髓模块尖峰活动的方差作为特征,这些灵感来自于疲劳感导致的神经肌肉适应特征。其次,我们将这些特征视为观测值,并开发了贝叶斯高斯过程来捕捉时间演变的进展。第三,我们通过数学形式化来解决缺乏监督信息的问题,将疲劳的时间演变特征作为损失函数。最后,我们适应了遵循疲劳生理原理的指标来量化评估表现。我们的广泛实验表明了与日相似度为0.99,与其他疲劳视图相似度超过0.7以及几乎1的弱单调性,表现优于其他方法。这项研究旨在客观评估肌肉疲劳。