A Statistical Physics Framework for Intermittent Neural Control of Human Balance
IEEE Transactions on Cybernetics
|April 20, 2026
Summary
Human balance relies on complex neural control. This study reveals intermittent neural feedback control can stabilize posture, but its stability depends on specific parameters and conditions, impacting fall-risk mitigation.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Control Theory
Background:
- Human quiet stance involves complex neural mechanisms for balance.
- The exact stabilization strategy for upright posture remains debated.
- Intermittent neural feedback control is a plausible mechanism for balance and sway.
Purpose of the Study:
- Investigate the stability of quiet stance under manifold-triggered intermittent neural feedback control.
- Analyze the impact of control manifolds on system stability.
- Develop a framework for analyzing noise-perturbed, delayed dynamics in intermittent control.
Main Methods:
- Systematic investigation of quiet stance stability under intermittent neural feedback control.
- Application of a statistical-physics-based approach for stability characterization.
- Theoretical analysis complemented by numerical validation.
- Lyapunov-based analyses for stability.
Main Results:
- The control manifold can destabilize or enhance stability depending on activation regions.
- Delineated stable parameter regions for intermittent neural control.
- Quantified how system parameters influence stability boundaries.
- Developed a framework applicable to noise-perturbed delayed dynamics.
Conclusions:
- Intermittent neural control stability is sensitive to parameter variations and activation conditions.
- The developed framework provides principled insights into noise-perturbed delayed nonlinear dynamics.
- Findings inform neural balance regulation, fall-risk mitigation, and exoskeleton control design.
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