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http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2398| A dynamic Bayesian network for estimating the risk of falls from real gait data | |
| GERMAN CUAYA SIMBRO ANGELICA MUÑOZ MELENDEZ LIDIA NUÑEZ CARRERA Eduardo Francisco Morales Manzanares IVETT QUIÑONES URIOSTEGUI ALDO ALESSI MONTERO | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| Probabilistic models Dynamic Bayesian networks Elderly Gait analysis Risk of falls | |
| 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. | |
| Springer | |
| 2013 | |
| Artículo | |
| Inglés | |
| Estudiantes Investigadores Público en general | |
| Cuaya, G., et al., (2013). A dynamic Bayesian network for estimating the risk of falls from real gait data, Medical & Biological Engineering & Computing, Vol. 2013 (51): 29–37 | |
| CIENCIA DE LOS ORDENADORES | |
| Versión aceptada | |
| acceptedVersion - Versión aceptada | |
| Aparece en las colecciones: | Artículos de Ciencias Computacionales |
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