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Estimation of human walking stability regions via zonotopic set-membership
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DOI：10.1002/asjc.70171.png)
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Abstract
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Falls in older adults present a critical public health challenge, underscoring the urgent need for reliable stability monitoring. Existing assessment methods, ranging from simplistic threshold-based approaches to probabilistic estimators, often lack either robustness to model uncertainties or interpretable safety guarantees. This paper presents a novel framework for real-time gait stability assessment by formulating it as a set-membership state estimation problem under unknown-but-bounded (UBB) uncertainties. We introduce a time-varying stability region, represented as a zonotope in the center of mass (COM) state space, which is empirically constructed from normal walking data. A zonotopic set-membership filter (SMF) is then developed to propagate bounded uncertainties and recursively compute an enclosing set of the current state. Stability is certified via a sufficient set-inclusion condition between the state set and the stability region; when this condition is violated, a convex distance-to-set metric provides a continuous risk score. Simulations and experimental studies demonstrate that the proposed estimator achieves interpretable, bounded-error tracking of COM states. The results confirm its ability to discriminate between normal and disturbed gait with interpretable risk escalation, offering a principled foundation for wearable fall prevention systems.
Keywords:
gait stability
inertial sensing
online risk assessment
robust state estimation
set-membership filtering
zonotope
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