The bionic neural network for external simulation of human locomotor system

TL;DR

This paper introduces a physics-informed deep learning approach that integrates musculoskeletal modeling into neural networks to efficiently predict human joint motion and muscle forces, overcoming computational challenges of traditional models.

Contribution

The novel method embeds MSK models into neural networks as an ODE loss, automatically estimating physiological parameters during training for personalized predictions.

Findings

Accurately predicts joint motion and muscle forces.

Effectively identifies subject-specific physiological parameters.

Demonstrates robustness on benchmark and real data.

Abstract

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.