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Fusion de données crédibilistes dans le cadre de l'intelligence ambiante

Abstract : The Smart Home concept aims at providing contextualized services to its inhabitants. Based on a heterogeneous sensors domestic network, this new kind of Smart Home deduces the most adapted action to realize with sensory data interpretation. The first problem concerns the strong heterogeneity in the sensor domain: sensors have their own hardware and software features, and their communication standards are poorly standardised. In this thesis, our interest is the context modelling and we propose a service oriented software architecture combining complementary and/or redundant sensors. We use the Transferable Belief Model (TBM) to merge sensor data and to take into account the uncertain nature of information. This model is a variant of the Dempster-Shafer theory. The sensors reliability is taken into account during the merging process to weight a failing sensor. A sensor failure can prevent context data building. The sensor reliability is estimated with a pairwise sensor fusion. The temporal conflict analysis allows detection and identification of a failing sensor. We present a second method aiming at detecting temporal behaviour drift. A TBM fusion between a predicted symbolic state and the observed symbolic state provided by the sensor is achieved. The predicted symbolic state estimation is based on a known model of behaviour. The temporal conflict analysis allows detecting behaviour drifts. Finally, we present a case study where the previous approaches are implemented in cascade in order to detect a falling person.
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Submitted on : Wednesday, November 18, 2020 - 11:45:43 AM
Last modification on : Tuesday, November 23, 2021 - 9:44:29 AM
Long-term archiving on: : Friday, February 19, 2021 - 7:05:00 PM


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  • HAL Id : tel-03011784, version 1


Vincent Ricquebourg. Fusion de données crédibilistes dans le cadre de l'intelligence ambiante. Automatique. Université de Valenciennes et du Hainaut-Cambrésis, 2008. Français. ⟨NNT : 2008VALE0027⟩. ⟨tel-03011784⟩



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