Biomechanical Optimization and Reinforcement Learning Provide Insights into Ankle-to-Hip Strategy Transitions in Human Postural Control

Human postural control strategies, categorized as ankle or hip strategies, adapt to varying perturbation magnitudes and support surface sizes. While numerous studies have characterized these strategies, few have explored the underlying mechanisms driving the transition from ankle to hip strategy. This study investigated whether postural strategy transitions can be explained through an optimization mechanism incorporating biomechanical constraints. We analyzed postural strategy changes in human responses to backward perturbations and developed a reinforcement learning (RL)-based optimization model. The biomechanical constraint was defined as the center of pressure (CoP) range limitation to the metatarsal joint. The control objective function featured a novel CoP constraint penalty, complemented by terms for upright posture recovery and control effort minimization. The RL-based optimization model successfully reproduced the ankle-to-hip strategy transition observed in human postural responses. With increasing perturbation magnitude, the model demonstrated a pattern of limited ankle torque coupled with increased hip joint kinematics, closely aligning with observed human postural adaptations. These results suggest that the adaptive nature of human postural strategy transitions can be understood within an optimization framework incorporating biomechanical constraints. Additionally, this study supports the use of RL models, capable of implementing nonlinear optimization, as a valuable tool for comprehensively analyzing diverse adaptive characteristics in human movement.