用物理融合深度学习，估算深层肌肉电活动信号。
Deep Muscle EMG construction using A Physics-Integrated Deep Learning approach
- 融合物理规律与数据驱动，构建可泛化的深层肌电估计模型。
- 在5名受试者上验证，预测精度显著优于现有方法。
- 适合运动科学、康复工程等需深层肌电信号的研究者使用。
基于肌电图（EMG）的计算骨骼肌建模是一种无创方法，可用于研究肌肉肌腱功能、人体运动及神经肌肉控制，提供肌肉力和关节扭矩等内部变量的估计。然而，深层肌肉的肌电信号难以通过表面电极获取，直接侵入式测量又不可行。缺乏深层肌肉的肌电数据严重制约了该技术的广泛应用。为此，本文提出一种混合深度学习算法——神经骨骼肌模型（NMM），结合物理信息与数据驱动学习，以估算深层肌肉的肌电信号。数据驱动部分用于预测缺失信号，物理模型则嵌入个体特异性信息以提升预测准确性。在5名受试者上进行实验验证，结果表明，所提NMM在关节扭矩估计方面表现优异，并显著超越当前最先进的肌协同外推（MSE）方法。
原文摘要 · Abstract (English)
Electromyography (EMG)--based computational musculoskeletal modeling is a non-invasive method for studying musculotendon function, human movement, and neuromuscular control, providing estimates of internal variables like muscle forces and joint torques. However, EMG signals from deeper muscles are often challenging to measure by placing the surface EMG electrodes and unfeasible to measure directly using invasive methods. The restriction to the access of EMG data from deeper muscles poses a considerable obstacle to the broad adoption of EMG-driven modeling techniques. A strategic alternative is to use an estimation algorithm to approximate the missing EMG signals from deeper muscle. A similar strategy is used in physics-informed deep learning, where the features of physical systems are learned without labeled data. In this work, we propose a hybrid deep learning algorithm, namely the neural musculoskeletal model (NMM), that integrates physics-informed and data-driven deep learning to approximate the EMG signals from the deeper muscles. While data-driven modeling is used to predict the missing EMG signals, physics-based modeling engraves the subject-specific information into the predictions. Experimental verifications on five test subjects are carried out to investigate the performance of the proposed hybrid framework. The proposed NMM is validated against the joint torque computed from 'OpenSim' software. The predicted deep EMG signals are also compared against the state-of-the-art muscle synergy extrapolation (MSE) approach, where the proposed NMM completely outperforms the existing MSE framework by a significant margin.
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