Heterogeneous multi-sensor fusion based on an evidential network for fall detection
| Autor: | Bernadette Dorizzi, Dan Istrate, Joao C. M. Mota, Imad Belfeki, Jean Louis Baldinger, Hamid Medjahed, Toufik Guettari, Paulo Armando Cavalcante Aguilar, Jerome Boudy |
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| Přispěvatelé: | Département Electronique et Physique (EPH), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Laboratoire de Recherche et d'Innovation Technologique (LRIT), ESIGETEL, Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Centre National de la Recherche Scientifique (CNRS), Departamento de Engenharia de Teleinformática (DETI), Universidade Federal do Ceará = Federal University of Ceará (UFC) |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
020205 medical informatics
Computer science Dempster Shafer theory Bayesian probability 02 engineering and technology computer.software_genre Remote medical monitoring [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Dempster–Shafer theory Fall detection 0202 electrical engineering electronic engineering information engineering Sensitivity (control systems) Representation (mathematics) Structure (mathematical logic) Fusion [STAT.AP]Statistics [stat]/Applications [stat.AP] business.industry Pattern recognition Sensor fusion 020201 artificial intelligence & image processing Evidential network Artificial intelligence Data mining business computer [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | ICOST '11 : 9th International Conference on Smart Homes and Health Telematics ICOST '11 : 9th International Conference on Smart Homes and Health Telematics, Jun 2011, Montreal, Canada. pp.281-285, ⟨10.1007/978-3-642-21535-3_42⟩ Toward Useful Services for Elderly and People with Disabilities ISBN: 9783642215346 ICOST |
| Popis: | International audience; 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 alone system. |
| Databáze: | OpenAIRE |
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