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A scalable semantic framework for IoT healthcare applications

Abstract : IoT-based systems for early epidemic detection have not been investigated yet in the research. The state-of-the art in sensor technology and activity recognition makes it possible to automatically detect Activities of Daily Living (ADL). Semantic reasoning over ADLs can discover anomalies and symptoms for disorders, hence diseases and epidemics. However, semantic reasoning is computationally rather expensive and therefore unusable for real-time monitoring in large scale applications, like early epidemic detection. To overcome this limitation, this paper proposes a new scalable semantic framework based on several semantic reasoning techniques that are distributed over a semantic middleware. To reduce the number of events to process during the semantic reasoning, a Complex Event Processing (CEP) engine is used to detect abnormal events in ADL and to generate the associated symptom indicators. To demonstrate real-time detection and scalability, the proposed framework integrates a new extension of ADLSim, a discrete event simulator that simulates long-term sequences of ADL.
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https://hal.archives-ouvertes.fr/hal-03695152
Contributor : Rémi Bastide Connect in order to contact the contributor
Submitted on : Tuesday, June 14, 2022 - 3:10:31 PM
Last modification on : Wednesday, July 13, 2022 - 10:35:56 AM

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Rita Zgheib, Stein Kristiansen, Emmanuel Conchon, Thomas Plageman, Vera Goebel, et al.. A scalable semantic framework for IoT healthcare applications. Journal of Ambient Intelligence and Humanized Computing, Springer, 2020, ⟨10.1007/s12652-020-02136-2⟩. ⟨hal-03695152⟩

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