Muscle forces and joint kinematics estimated with musculoskeletal (MSK) modeling techniques offer useful metrics describing movement quality. Model-based computational MSK models can interpret the dynamic interaction between the neural drive to muscles, muscle dynamics, body and joint kinematics, and kinetics. Still, such a set of solutions suffers from high computational time and muscle recruitment problems, especially in complex modeling. In recent years, data-driven methods have emerged as a promising alternative due to the benefits of flexibility and adaptability. However, a large amount of labeled training data is not easy to be acquired. This paper proposes a physics-informed deep learning method based on MSK modeling to predict joint motion and muscle forces. The MSK model is embedded into the neural network as an ordinary differential equation (ODE) loss function with physiological parameters of muscle activation dynamics and muscle contraction dynamics to be identified. These parameters are automatically estimated during the training process which guides the prediction of muscle forces combined with the MSK forward dynamics model. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The results demonstrate that the proposed deep learning method can effectively identify subject-specific MSK physiological parameters and the trained physics-informed forward-dynamics surrogate yields accurate motion and muscle forces predictions.

翻译：基于肌肉骨骼建模技术估计的肌肉力和关节运动学提供了描述运动质量的有用指标。基于模型的计算性肌肉骨骼模型能够解释神经驱动与肌肉、肌肉动力学、身体及关节运动学与动力学之间的动态交互。然而，这类解决方案存在计算时间长、肌肉募集问题突出等缺陷，尤其在复杂建模场景中。近年来，数据驱动方法因其灵活性和适应性优势成为有前景的替代方案，但获取大量带标签的训练数据并非易事。本文提出一种基于肌肉骨骼建模的物理信息深度学习方法来预测关节运动和肌肉力。该方法将肌肉骨骼模型作为常微分方程损失函数嵌入神经网络，其中包含待识别的肌肉激活动力学和肌肉收缩动力学生理参数。这些参数在训练过程中自动估计，并结合肌肉骨骼正向动力学模型指导肌肉力预测。通过两组数据（包括一个基准数据集和来自六名健康受试者的自采集数据集）进行实验验证。结果表明，所提出的深度学习方法能有效识别特定受试者的肌肉骨骼生理参数，且训练后的物理信息正向动力学代理模型可生成精确的运动与肌肉力预测。