Med Biol Eng Comput - A dynamic Bayesian network for estimating the risk of falls from real gait data.

Tópicos

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Resumo

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 computational) 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 pathological gait.

Resumo Limpo

patholog agerel chang may affect individu gait turn rais risk fall elder fall common may eventu sever injuri longterm disabl even death thus interest estim risk fall gait analysi estim risk fall requir consider longitudin evolut differ variabl deriv human gait bayesian network probabilist model graphic express depend among variabl dynam bayesian network dbns type bn adequ model dynam statist depend set variabl work dbn model incorpor gait deriv variabl predict risk fall elder within month subsequ gait assess two dbns develop first dbn expertguid built use gait variabl identifi domain expert wherea second dbn strict comput construct util gait variabl pick featur select algorithm effect second model predict fall month follow assess result encourag suppli evid regard use dynam probabilist model predict fall patholog gait

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