A Statistical Physics Framework for Intermittent Neural Control of Human Balance

Human quiet stance appears to be a simple postural task, yet it relies on complex neural control mechanisms to maintain balance. Although quiet stance control has been studied extensively, the underlying stabilization strategy remains debated. Intermittent neural feedback control is one of the few plausible mechanisms that can simultaneously stabilize upright posture and account for the characteristic sway observed during standing. However, intermittent control during quiet stance constitutes a distinctive class of nonlinear control characterized by state-dependent switching and manifold-triggered activation. These properties complicate the application of conventional nonlinear control frameworks to rigorous stability analysis. Consequently, much of the existing literature has relied predominantly on numerical simulations rather than analytical results. In this study, we systematically investigated the stability of quiet stance under manifold-triggered intermittent neural feedback control, with particular emphasis on marginal stability. We found that the control manifold destabilized the system in some activation regions but enhanced stability in others. To address the theoretical challenges posed by delays and stochastic noise, we further developed a statistical-physics-based approach for stability characterization that complements Lyapunov-based analyses and is applicable to noise-perturbed delayed dynamics. Using this framework, we delineated the stable parameter regions for intermittent neural control and quantified how key system parameters shape the stability boundaries through both theoretical analysis and numerical validation. Together, these results provide a principled perspective for studying noise-perturbed delayed nonlinear dynamics in intermittent control models of quiet stance, offering insights into neural balance regulation and informing potential applications such as fall-risk mitigation and the control design of lower-limb exoskeletons.