Citation

Cavalcante Aguilar PA, Boudy J, Istrate D, Dorizzi B, Moura Mota JC. IEEE J. Biomed. Health Inform. 2014; 18(4): 1103-1113.

Copyright

(Copyright © 2014, Institute of Electrical and Electronics Engineers)

DOI

10.1109/JBHI.2013.2283055

PMID

24235255

Abstract

This work is part of the development of a remote home healthcare monitoring application designed to detect distress situations through several types of sensors. The multi-sensor 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 multi-sensor 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 work 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 non-stationary nature may degrade the experimental conditions. To compensate the non-stationary effect, the time evolution is consider by introducing the Dynamic Evidential Networks which was evaluated by the simulated fall scenarios corresponding to various use cases.

Language: en