Modeling the neuro-mechanics of human balance when recovering from a fall: a continuous-time approach
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Modeling the neuro‑mechanics of human balance when recovering from a fall: a continuous‑time approach Angel Cerda‑Lugo1, Alejandro González2* , Antonio Cardenas1 and Davide Piovesan3 *Correspondence: [email protected] 2 Faculty of Engineering, CONACyT-Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico Full list of author information is available at the end of the article
Abstract Background: Balance control deteriorates with age and nearly 30% of the elderly population in the United States reports stability problems. Postural stability is an integral task to daily living reliant upon the control of the ankle and hip. To this end, the estimation of joint parameters can be a useful tool when analyzing compensatory actions aimed at maintaining postural stability. Methods: Using an analytical approach, this study expands on previous work and ana‑ lyzes a two degrees of freedom human model. The first two modes of vibration of the system are represented by the neuro-mechanical parameters of a second-order, timevarying Kelvin–Voigt model actuated at the ankle and hip. The model is tested using a custom double inverted pendulum and healthy volunteers who were subjected to a positional step-like perturbation during quiet standing. An in silico sensitivity analysis of the influence of inertial parameters was also performed. Results: The proposed method is able to correctly identify the time-varying viscoelastic parameters of of a double inverted pendulum. We show that that the param‑ eter estimation method can be applied to standing humans. These results appear to identify a subject-independent strategy to control quiet standing that combines both the modulation of stiffness, and the use of an intermittent control. Conclusions: This paper presents the analysis of the non-linear system of differential equations representing the control of lumped muscle–tendon units. It utilizes motion capture measurements to obtain the estimates of the system’s control parameters by constructing a simple time-dependent regressor for estimating the time-varying parameters of the control with a single perturbation. This work is a step forward into the understanding of the neuro-mechanical control parameters of human recovering from a fall. In previous literature, the analysis is either restricted to the first vibrational mode of an inverted-pendulum model or assumed to be time-invariant. The proposed method allows for the analysis of hip related movement for stability control and high‑ lights the importance of core training. Keywords: Human balance, Parameter estimation, Dynamic model
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