Heterogeneous multi-sensor fusion based on an evidential network for fall detection

  • Authors:

  • Paulo Armando Cavalcante Aguilar;Jerome Boudy;Dan Istrate;Hamid Medjahed;Bernadette Dorizzi;João Cesar Moura Mota;Jean Louis Baldinger;Toufik Guettari;Imad Belfeki

  • Affiliations:

  • Télécom SudParis, Electronic and Physic department, Evry France;Télécom SudParis, Electronic and Physic department, Evry France;ESIGETEL, Avon, France;ESIGETEL, Avon, France;Télécom SudParis, Electronic and Physic department, Evry France;Federal University of Ceara, Fortaleza, Brazil;Télécom SudParis, Electronic and Physic department, Evry France;-;Télécom SudParis, Electronic and Physic department, Evry France

  • Venue:
  • ICOST'11 Proceedings of the 9th international conference on Toward useful services for elderly and people with disabilities: smart homes and health telematics
  • Year:
  • 2011

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Abstract

The multi-sensor fusion can provide more accurate and reliable information compared to information from each sensor separately taken. Moreover, the data from multiple heterogeneous sensors present in the medical surveillance systems have different degrees of uncertainty. Among multi-sensor data fusion techniques, Bayesian methods and evidence theories such as Dempster-Shafer Theory (DST), are commonly used to handle the degree of uncertainty in the fusion processes. Based on a graphic representation of the DST called evidential networks, we propose a structure of heterogeneous multisensor fusion for falls detection. The proposed Evidential Network (EN) can handle the uncertainty present in a mobile and a fixed sensor-based remote monitoring systems (fall detection) by fusing them and therefore increasing the fall detection sensitivity compared to the a separated alone system.