Bayesian inference of physiologically meaningful parameters from body sway measurements

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...

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Autores principales:	A. Tietäväinen, M. U. Gutmann, E. Keski-Vakkuri, J. Corander, E. Hæggström
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e4ea9dda196441e6b20402e1e052651c
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spelling	oai:doaj.org-article:e4ea9dda196441e6b20402e1e052651c2021-12-02T16:06:35ZBayesian inference of physiologically meaningful parameters from body sway measurements10.1038/s41598-017-02372-12045-2322https://doaj.org/article/e4ea9dda196441e6b20402e1e052651c2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02372-1https://doaj.org/toc/2045-2322Abstract 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.A. TietäväinenM. U. GutmannE. Keski-VakkuriJ. CoranderE. HæggströmNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-14 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q A. Tietäväinen M. U. Gutmann E. Keski-Vakkuri J. Corander E. Hæggström Bayesian inference of physiologically meaningful parameters from body sway measurements
description	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.
format	article
author	A. Tietäväinen M. U. Gutmann E. Keski-Vakkuri J. Corander E. Hæggström
author_facet	A. Tietäväinen M. U. Gutmann E. Keski-Vakkuri J. Corander E. Hæggström
author_sort	A. Tietäväinen
title	Bayesian inference of physiologically meaningful parameters from body sway measurements
title_short	Bayesian inference of physiologically meaningful parameters from body sway measurements
title_full	Bayesian inference of physiologically meaningful parameters from body sway measurements
title_fullStr	Bayesian inference of physiologically meaningful parameters from body sway measurements
title_full_unstemmed	Bayesian inference of physiologically meaningful parameters from body sway measurements
title_sort	bayesian inference of physiologically meaningful parameters from body sway measurements
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/e4ea9dda196441e6b20402e1e052651c
work_keys_str_mv	AT atietavainen bayesianinferenceofphysiologicallymeaningfulparametersfrombodyswaymeasurements AT mugutmann bayesianinferenceofphysiologicallymeaningfulparametersfrombodyswaymeasurements AT ekeskivakkuri bayesianinferenceofphysiologicallymeaningfulparametersfrombodyswaymeasurements AT jcorander bayesianinferenceofphysiologicallymeaningfulparametersfrombodyswaymeasurements AT ehæggstrom bayesianinferenceofphysiologicallymeaningfulparametersfrombodyswaymeasurements
_version_	1718384996699340800

Bayesian inference of physiologically meaningful parameters from body sway measurements

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