Abstract
To follow up our works on a service-oriented architecture dedicated to context perception for the smart home [7], we present in this paper how to move complex reasoning on top of the ontology. We focus on a multi-sensors data fusion architecture using the Transferable Belief Model. High level symbolic data are deduced using fusion from redundant, complementary and heterogeneous sensors data. Symbolic data describe complex contexts and enable to provide more contextualised services. To show the contribution of using the Transferable Belief Model in context awareness systems we detail an experiment about human posture characterisation using heterogeneous and uncertain sensors data.
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