Evidential Network-Based Multimodal Fusion for Fall Detection

Paulo Armando Cavalcante Aguilar, Jerome Boudy, Dan Istrate, Hamid Medjahed, Bernadette Dorizzi, João Cesar Moura Mota, Jean Louis Baldinger, Toufik Guettari and Imad Belfeki
Additional contact information
Paulo Armando Cavalcante Aguilar: Electronic and Physics Department, Mines Télécom- Télécom SudParis, Evry, France
Jerome Boudy: Electronic and Physics Department, Mines Télécom- Télécom SudParis, Evry, France
Dan Istrate: École Supérieure d’Ingénieurs en Informatique et Génie des Télécommunications, Villejuif, France
Hamid Medjahed: École Supérieure d’Ingénieurs en Informatique et Génie des Télécommunications, Villejuif, France
Bernadette Dorizzi: Electronic and Physics Department, Mines Télécom- Télécom SudParis, Evry, France
João Cesar Moura Mota: Federal University of Ceará, Benfica, Fortaleza, Brazil
Jean Louis Baldinger: Electronic and Physics Department, Mines Télécom- Télécom SudParis, Evry, France
Toufik Guettari: Electronic and Physics Department, Mines Télécom- Télécom SudParis, Evry, France
Imad Belfeki: Electronic and Physics Department, Mines Télécom- Télécom SudParis, Evry, France

International Journal of E-Health and Medical Communications (IJEHMC), 2013, vol. 4, issue 1, 46-60

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 multi-sensor 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 system alone.

Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
https://services.igi-global.com/resolvedoi/resolve ... 018/jehmc.2013010105 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jehmc0:v:4:y:2013:i:1:p:46-60

Access Statistics for this article

International Journal of E-Health and Medical Communications (IJEHMC) is currently edited by Joel J.P.C. Rodrigues

More articles in International Journal of E-Health and Medical Communications (IJEHMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().