Recruitment of mechanics and motor control in human hopping, discovered by stance phase ground-level downward perturbations.

This study aims to enhance our understanding of human locomotion's adaptability to ground-level downward perturbations, focusing on hopping at preferred frequencies. By categorizing perturbations into early (ESP), mid (MSP), and late (LSP) stance phase and by analyzing the resulting biomechanical responses, we develop and validate a model that accurately replicates and predicts these behaviors. The spring-loaded inverted pendulum (SLIP) model, while capturing basic hopping dynamics, was inadequate for explaining subjects' responses. We introduced the sensory modulated spring (SMS) model, incorporating force, length, and velocity feedback (VFB), with gains optimized through genetic algorithms for enhanced accuracy. Our findings indicate distinct response patterns based on perturbation timing, highlighting the complexity of human adaptive mechanisms. The SMS model outperformed the SLIP model in replicating normal hopping behavior, while length and force feedback enable stable and economic human-like hopping, and VFB enables replicating humans' transient response to perturbation. Inspired by energy flow and behavioral changes in the experimental data, we introduced an extended SMS model with event-based adaptation at maximum compression and apex moment. The capability of this model to predict human perturbation recovery in hopping is demonstrated through systematic evaluation, including stability analyses and assessment of transient and steady-state responses. This study advances template-based modeling by integrating high-level reflexes besides local sensory feedback, offering a novel tool for understanding the inherent adaptability of human locomotion. The introduced adaptive model provides a novel framework for future research on adjustments to environmental challenges, with potential applications in designing effective rehabilitation protocols and assistive locomotion devices.