Bayesian inference of physiologically meaningful parameters from body sway measurements

A. Tietäväinen,M. U. Gutmann,E. Hæggström,E. Keski-Vakkuri,J. Corander

doi:10.1038/s41598-017-02372-1

A. Tietäväinen, M. U. Gutmann + Show 3 more

Open Access

https://doi.org/10.1038/s41598-017-02372-1

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Abstract

The control of the human body sway by the central nervous system, muscles, and conscious brain is of interest since body sway carries information about the physiological status of a person. Several models have been proposed to describe body sway in an upright standing position, however, due to the statistical intractability of the more realistic models, no formal parameter inference has previously been conducted and the expressive power of such models for real human subjects remains unknown. Using the latest advances in Bayesian statistical inference for intractable models, we fitted a nonlinear control model to posturographic measurements, and we showed that it can accurately predict the sway characteristics of both simulated and real subjects. Our method provides a full statistical characterization of the uncertainty related to all model parameters as quantified by posterior probability density functions, which is useful for comparisons across subjects and test settings. The ability to infer intractable control models from sensor data opens new possibilities for monitoring and predicting body status in health applications.

Highlights

Upright stance is inherently unstable due to the physics of an inverted pendulum-like body and due to the internal perturbations of an individual, such as noise in afferent and efferent nerve pathways, respiration, and hemodynamics[1,2,3,4]
This study was conducted to determine whether a single-link inverted pendulum (SLIPM) model with intermittent control together with approximate Bayesian computation can infer sway signals and parameters that are plausible for human subjects
The performance of the Approximate Bayesian computation (ABC) inference approach was quantified for simulated test subjects by calculating the fractional error and the goodness of fit between true and estimated parameters

Summary

Introduction

Upright stance is inherently unstable due to the physics of an inverted pendulum-like body and due to the internal perturbations of an individual, such as noise in afferent (sensory) and efferent (motor) nerve pathways, respiration, and hemodynamics[1,2,3,4]. More recent models feature PID (proportional-integrative-derivative)[20], PD (proportional-derivative)[21, 22], or optimal[29] active control together with passive control to maintain balance. These controllers act either continuously[20] or intermittently[21, 22], that is, only when they are needed. We focus here on the model presented by Asai et al in 2009 where the body is depicted as a single-link inverted pendulum (SLIPM)[21]. In the Asai model the body is kept upright by an active and a passive PD (proportional, derivative) controller.

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