Evidential network-based multimodal fusion for fall detection - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2011

Evidential network-based multimodal fusion for fall detection

Résumé

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.
Fichier principal
Vignette du fichier
phealth_2011_evidential_network_based_on_a_multi-sensor_fusion_for_fall_detection.pdf (149.83 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00707780 , version 1 (13-06-2012)

Identifiants

  • HAL Id : hal-00707780 , version 1

Citer

Paulo Armando Cavalcante Aguilar, Jérôme Boudy, Dan Istrate, Hamid Medjahed, Bernadette Dorizzi, et al.. Evidential network-based multimodal fusion for fall detection. pHealth '11 : 8th International Conference on Wearable Micro and Nano Technologies for Personalised Health, Jun 2011, Lyon, France. ⟨hal-00707780⟩
279 Consultations
193 Téléchargements

Partager

Gmail Facebook X LinkedIn More