Costa-Garcia Alvaro, Shimoda Shingo, Murai Akihiko
Research Institute on Human and Societal Augmentation, National Institute of Advanced Industrial Science and Technology (AIST), Kashiwa, Chiba, Japan.
Graduate School of Medicine, Nagoya University, Nagoya, Aichi, Japan.
PLoS One. 2025 Jun 12;20(6):e0319162. doi: 10.1371/journal.pone.0319162. eCollection 2025.
This study introduces an advanced computational model for simulating surface electromyography (sEMG) signals during muscle contractions. The model integrates five elements that simulate the chain of processes from motor intention to voltage variations over the skin. These elements include the motor control system, motor neurons, muscle fibers, biological tissues, and electrodes. sEMG signals were simulated for isotonic and isometric contractions under two force conditions and compared with real data obtained from elbow flexion experiments. The results demonstrate a high level of similarity between simulated and real signals, encompassing both temporal and spectral features. Additionally, the study reveals a correlation between muscle fiber type distribution and changes in the spectral distribution of the simulated signals. Potential applications of this research include the development of comprehensive sEMG databases and elucidating the relationship between sEMG signal characteristics and internal neuromuscular parameters. Future research aims to further explore these applications and enhance the model's performance by leveraging emerging technologies such as machine learning. This approach establishes a framework for simulating sEMG signals under tailored neuromuscular conditions and holds promise for advancing our understanding of muscular physiology and human motor control mechanisms.
本研究介绍了一种先进的计算模型，用于模拟肌肉收缩期间的表面肌电图（sEMG）信号。该模型整合了五个要素，模拟从运动意图到皮肤表面电压变化的一系列过程。这些要素包括运动控制系统、运动神经元、肌纤维、生物组织和电极。在两种力条件下对等张收缩和等长收缩的sEMG信号进行了模拟，并与从肘部屈曲实验获得的实际数据进行了比较。结果表明，模拟信号与实际信号在时间和频谱特征上具有高度相似性。此外，该研究揭示了肌纤维类型分布与模拟信号频谱分布变化之间的相关性。本研究的潜在应用包括开发综合sEMG数据库以及阐明sEMG信号特征与内部神经肌肉参数之间的关系。未来的研究旨在进一步探索这些应用，并通过利用机器学习等新兴技术提高模型的性能。这种方法建立了一个在特定神经肌肉条件下模拟sEMG信号的框架，有望推进我们对肌肉生理学和人类运动控制机制的理解。
