Título

A dynamic Bayesian network for estimating the risk of falls from real gait data

Autor

GERMAN CUAYA SIMBRO

ANGELICA MUÑOZ MELENDEZ

LIDIA NUÑEZ CARRERA

Eduardo Francisco Morales Manzanares

IVETT QUIÑONES URIOSTEGUI

ALDO ALESSI MONTERO

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by-nc-nd/4.0

Materias

Probabilistic models - (PROBABILISTIC MODELS) Dynamic Bayesian networks - (DYNAMIC BAYESIAN NETWORKS) Elderly - (ELDERLY) Gait analysis - (GAIT ANALYSIS) Risk of falls - (RISK OF FALLS) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)

Resumen o descripción

Pathological and age-related changes may affect an individual’s gait, in turn raising the risk of falls. In elderly, falls are common and may eventuate in severe injuries, long-term disabilities, and even death. Thus, there is interest in estimating the risk of falls from gait analysis. Estimation of the risk of falls requires consideration of the longitudinal evolution of different variables derived from human gait. Bayesian networks are probabilistic models which graphically express dependencies among variables. Dynamic Bayesian networks (DBNs) are a type of BN adequate for modeling the dynamics of the statistical dependencies in a set of variables. In this work, a DBN model incorporates gait derived variables to predict the risk of falls in elderly within 6 months subsequent to gait assessment. Two DBNs were developed; the first (DBN1; expert-guided) was built using gait variables identified by domain experts, whereas the second (DBN2; strictly com- putational) was constructed utilizing gait variables picked out by a feature selection algorithm. The effectiveness of the second model to predict falls in the 6 months following assessment is 72.22 %. These results are encouraging and supply evidence regarding the usefulness of dynamic probabilistic models in the prediction of falls from patho- logical gait.

Editor

Springer

Fecha de publicación

2013

Tipo de publicación

Artículo

Versión de la publicación

Versión aceptada

Recurso de información

http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2398

Formato

application/pdf

Idioma

Inglés

Relación

&

Biological Engineering

&

Computing, Vol. 2013 (51): 29–37

Audiencia

Estudiantes

Investigadores

Público en general

Sugerencia de citación

Cuaya, G., et al., (2013). A dynamic Bayesian network for estimating the risk of falls from real gait data, Medical

Repositorio Orígen

Repositorio Institucional del INAOE

Descargas

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