IEEE J Biomed Health Inform - A dynamic evidential network for fall detection.

Tópicos

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Resumo

This study is part of the development of a remote home healthcare monitoring application designed to detect distress situations through several types of sensors. The multisensor fusion can provide more accurate and reliable information compared to information provided by each sensor separately. Furthermore, data from multiple heterogeneous sensors present in the remote home healthcare monitoring systems have different degrees of imperfection and trust. Among the multisensor fusion methods, Dempster-Shafer theory (DST) is currently considered the most appropriate for representing and processing the imperfect information. Based on a graphical representation of the DST called evidential networks, a structure of heterogeneous data fusion from multiple sensors for fall detection has been proposed. The evidential networks, implemented on our remote medical monitoring platform, are also proposed in this paper to maximize the performance of automatic fall detection and thus make the system more reliable. However, the presence of noise, the variability of recorded signals by the sensors, and the failing or unreliable sensors may thwart the evidential networks performance. In addition, the sensors signals nonstationary nature may degrade the experimental conditions. To compensate the nonstationary effect, the time evolution is considered by introducing the dynamic evidential network which was evaluated by the simulated fall scenarios corresponding to various use cases.

Resumo Limpo

studi part develop remot home healthcar monitor applic design detect distress situat sever type sensor multisensor fusion can provid accur reliabl inform compar inform provid sensor separ furthermor data multipl heterogen sensor present remot home healthcar monitor system differ degre imperfect trust among multisensor fusion method dempstershaf theori dst current consid appropri repres process imperfect inform base graphic represent dst call evidenti network structur heterogen data fusion multipl sensor fall detect propos evidenti network implement remot medic monitor platform also propos paper maxim perform automat fall detect thus make system reliabl howev presenc nois variabl record signal sensor fail unreli sensor may thwart evidenti network perform addit sensor signal nonstationari natur may degrad experiment condit compens nonstationari effect time evolut consid introduc dynam evidenti network evalu simul fall scenario correspond various use case

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