The potential of reinforcement learning for lumbar load prediction in multi body models of the spine

Engineering of Sport 15 - Proceedings from the 15th International Conference on the Engineering of Sport (ISEA 2024)

Multi body models (MBS) of the spine are an integral part of clinical and biomechanical research. Their noninvasive and adaptive character makes them a promising tool to address a large variety of questions regarding spinal loading, its causes, and consequences for the healthy and pathological spine. In sports science and athletic training, the predictive simulations of human movement can explain how changes in training, technique, or equipment affect performance and the body's biomechanical responses, helping athletes and coaches to make informed decisions. For solving predictive simulations optimization algorithms like collocation method are the most performant ones. The Reinforcement Learning (RL) algorithms propose a transition from optimization to learning. They formulate the problem of predictive simulation as learning to generate new data that describes human movement. This formulation enables them to leverage the generative power of the Artificial Neural Networks (ANNs), capable of learning models from high-dimensional and nonlinear movement data, and subsequently using these models to generate new ones within new boundary conditions. Most studies including MBS of the spine use a combination of inverse kinematics and optimization for muscle force and lumbar load estimation. However, these approaches either use generic assumptions for loading tasks of low complexity or require kinematic data from experimental studies. In this study, we address the following question: What is the potential of RL algorithms for predicting the spine’s joint torques during an extension?