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.

翻译：基于肌电图（EMG）的计算肌肉骨骼建模是一种研究肌肉肌腱功能、人体运动和神经肌肉控制的非侵入性方法，可提供肌肉力、关节力矩等内部变量的估计值。然而，通过表面肌电电极测量深层肌肉的肌电信号通常具有挑战性，且采用侵入式方法直接测量往往不可行。深层肌肉肌电数据获取受限的问题，严重阻碍了EMG驱动建模技术的广泛应用。一种策略性替代方案是采用估计算法来近似缺失的深层肌肉肌电信号。这与物理信息深度学习所采用的策略相似——后者可在无标注数据的情况下学习物理系统的特征。本研究提出一种混合深度学习算法，即神经肌肉骨骼模型（NMM），该模型融合物理信息与数据驱动的深度学习来近似深层肌肉的肌电信号。数据驱动建模用于预测缺失的肌电信号，而基于物理的建模则将受试者特异性信息嵌入预测结果。通过对五名测试对象进行实验验证，评估了所提混合框架的性能。所提出的NMM模型通过'OpenSim'软件计算的关节力矩进行了验证。预测的深层肌电信号还与最先进的肌肉协同外推（MSE）方法进行了对比，结果表明NMM模型以显著优势全面超越了现有MSE框架。