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.

该研究论文提出了一种基于肌骨建模的受物理学约束的深度学习方法，用于预测关节运动和肌肉力量，并证明该方法能有效地识别个体特定的肌骨生理参数，并且训练的受物理学约束的前向动力学模型能够准确地预测运动和肌肉力量。