Estimating Continuous Muscle Fatigue For Multi-Muscle Coordinated Exercise: A Pilot Study on Walking
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929609023422464 |
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| author | Yi, Chunzhi Sun, Xiaolei Zhang, Chunyu Jin, Wei Zhu, Jianfei Zhu, Haiqi Wei, Baichun |
| author_facet | Yi, Chunzhi Sun, Xiaolei Zhang, Chunyu Jin, Wei Zhu, Jianfei Zhu, Haiqi Wei, Baichun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_17614 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Estimating Continuous Muscle Fatigue For Multi-Muscle Coordinated Exercise: A Pilot Study on Walking Yi, Chunzhi Sun, Xiaolei Zhang, Chunyu Jin, Wei Zhu, Jianfei Zhu, Haiqi Wei, Baichun Human-Computer Interaction Artificial Intelligence Signal Processing 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. |
| title | Estimating Continuous Muscle Fatigue For Multi-Muscle Coordinated Exercise: A Pilot Study on Walking |
| topic | Human-Computer Interaction Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2303.17614 |