Teramae Tatsuya, Matsubara Takamitsu, Noda Tomoyuki, Morimoto Jun
Department of Brain Robot Interface, Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International, Kyoto, Japan.
The Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara, Japan.
Front Bioeng Biotechnol. 2025 Jul 25;13:1611414. doi: 10.3389/fbioe.2025.1611414. eCollection 2025.
Electromyography (EMG) is essential for accurate assessment of motor function in rehabilitation, sports science, and robotics. However, its various time-consuming human operations (e.g., electromagnetic noise countermeasures) limit its widespread use. Meanwhile, motion capture technology has become more accessible, leading to increasing interest in musculoskeletal simulation models such as OpenSim. Although advances have been made in individualizing the model parameters, accurately estimating muscle activity remains a significant challenge. Previous efforts to optimize the parameters in musculoskeletal model simulators have yielded limited improvements in estimation accuracy. A key source of error that is identified in this study is the spatio-temporal distortion between the estimated and actual muscle activity, which has been inadequately addressed in previous research. To address this problem, this study proposes the Neural-Enhanced Motion-to-EMG (NEM2E) framework, which mitigates spatio-temporal distortions in simulated muscle activity using the Spatio-Temporal Distortion Refinement Network (STDR-Net). The STDR-Net is implemented via a Sequence-to-Sequence model with attention mechanisms to refine the estimates. Validation on two public datasets (walking and running motions) confirms significant accuracy improvements: enhanced estimations for all five muscles in the running dataset and for two of five muscles in the walking dataset. These findings demonstrate the potential of the NEM2E framework to refine OpenSim-generated muscle activity estimates and advance personalized applications in muscle activity analysis.
肌电图（EMG）对于在康复、运动科学和机器人技术中准确评估运动功能至关重要。然而，其各种耗时的人工操作（例如电磁噪声对策）限制了它的广泛应用。与此同时，运动捕捉技术已变得更加容易获取，这使得人们对诸如OpenSim等肌肉骨骼模拟模型的兴趣日益增加。尽管在使模型参数个性化方面已经取得了进展，但准确估计肌肉活动仍然是一项重大挑战。此前在肌肉骨骼模型模拟器中优化参数的努力在估计精度方面取得的改进有限。本研究确定的一个关键误差来源是估计的肌肉活动与实际肌肉活动之间的时空失真，而此前的研究对此处理不足。为了解决这个问题，本研究提出了神经增强运动到肌电图（NEM2E）框架，该框架使用时空失真细化网络（STDR-Net）减轻模拟肌肉活动中的时空失真。STDR-Net通过具有注意力机制的序列到序列模型来实现，以细化估计值。在两个公共数据集（行走和跑步运动）上的验证证实了显著的精度提高：跑步数据集中所有五块肌肉以及行走数据集中五块肌肉中的两块肌肉的估计值得到了增强。这些发现证明了NEM2E框架在细化OpenSim生成的肌肉活动估计值以及推进肌肉活动分析中的个性化应用方面的潜力。
