Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG
Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG
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DOI：
10.1109/tnsre.2022.3226860
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发表时间：
2023-01-01
影响因子：
4.9
通讯作者：
Zhang, Zhi-Qiang
中科院分区：
文献类型：
--
作者：
Zhang, Jie;Zhao, Yihui;Zhang, Zhi-Qiang
肌肉骨骼模型已经被广泛用于详细的生物力学分析，以考虑到它们估计运动变量（即，肌肉力和关节力矩），其不能在体内容易地测量。基于物理的计算神经肌肉骨骼模型可以解释神经驱动到肌肉、肌肉动力学、身体和关节运动学和动力学之间的动态相互作用。然而，这样的一组解决方案仍然存在速度慢的问题，特别是对于复杂的模型，这阻碍了实时应用中的实用性。近年来，数据驱动的方法已经成为一个有前途的替代方案，由于在快速和简单的实施的好处，但他们不能反映潜在的神经力学过程。本文提出了一种用于肌肉骨骼建模的物理信息深度学习框架，其中将基于物理的领域知识作为软约束引入数据驱动模型，以惩罚/规范数据驱动模型。我们使用的同步肌肉力量和关节运动学预测的表面肌电图（sEMG）为例来说明所提出的框架。卷积神经网络（CNN）被用作深度神经网络来实现所提出的框架。同时，利用肌肉力与关节运动学之间的物理规律进行软约束.实验验证两组数据，包括一个基准数据集和一个自我收集的数据集，从六个健康的人，进行。实验结果证明了该框架的有效性和鲁棒性。
Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moments) which cannot be readily measured in vivo. Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction between neural drive to muscles, muscle dynamics, body and joint kinematics and kinetics. Still, such set of solutions suffers from slowness, especially for the complex models, hindering the utility in real-time applications. In recent years, data-driven methods have emerged as a promising alternative due to the benefits in speedy and simple implementation, but they cannot reflect the underlying neuromechanical processes. This paper proposes a physics-informed deep learning framework for musculoskeletal modelling, where physics-based domain knowledge is brought into the data-driven model as soft constraints to penalise/regularise the data-driven model. We use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework. Simultaneously, the physics law between muscle forces and joint kinematics is used the soft constraint. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The experimental results demonstrate the effectiveness and robustness of the proposed framework.