A scalable semantic framework for IoT healthcare applications

dc.contributor.author Zgheib, Rita
dc.contributor.author Kristiansen, Stein
dc.contributor.author Conchon, Emmanuel
dc.contributor.author Plageman, Thomas
dc.contributor.author Goebel, Vera
dc.contributor.author Bastide, Rémi
dc.date.accessioned 2020-06-30T08:07:54Z
dc.date.available 2020-06-30T08:07:54Z
dc.date.copyright 2020
dc.date.issued 2020
dc.description This article is not available at CUD collection. The version of scholarly record of this article paper is published in 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) (2020), available online at: https://doi.org/10.1007/s12652-020-02136-2 en_US
dc.description.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. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature. en_US
dc.description.sponsorship European Cooperation in Science and Technology Norges Forskningsråd en_US
dc.identifier.citation Zgheib, R., Kristiansen, S., Conchon, E., Plageman, T., Goebel, V., & Bastide, R. (2020). A scalable semantic framework for IoT healthcare applications. Journal of Ambient Intelligence and Humanized Computing, https://doi.org/10.1007/s12652-020-02136-2 en_US
dc.identifier.issn 18685137
dc.identifier.uri https://doi.org/10.1007/s12652-020-02136-2
dc.identifier.uri http://hdl.handle.net/20.500.12519/223
dc.language.iso en en_US
dc.publisher Springer en_US
dc.relation Authors Affiliations : Zgheib, R., Canadian University Dubai, Dubai, United Arab Emirates; Kristiansen, S., Department of Informatics, University of Oslo, Oslo, 0316, Norway; Conchon, E., University of Limoges, CNRS, XLIM, UMR 7252, Limoges, 87000, France; Plageman, T., Department of Informatics, University of Oslo, Oslo, 0316, Norway; Goebel, V., Department of Informatics, University of Oslo, Oslo, 0316, Norway; Bastide, R., University of Toulouse, IRIT/ISIS, Castres, 81100, France
dc.relation.ispartofseries Journal of Ambient Intelligence and Humanized Computing;
dc.rights License to reuse the abstract has been secured from Springer Nature and Sons and Copyright Clearance Center.
dc.rights.holder Copyright : © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
dc.rights.uri https://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=47a2718b-4ce3-4c05-afb7-28b41696fb06
dc.subject Epidemic detection en_US
dc.subject Middleware en_US
dc.subject Ontologies en_US
dc.subject Simulated activities en_US
dc.subject Epidemiology en_US
dc.subject Semantics en_US
dc.subject Activities of Daily Living en_US
dc.subject Activity recognition en_US
dc.subject Complex event processing (CEP) en_US
dc.subject Discrete-event simulators en_US
dc.subject Health care application en_US
dc.subject Large-scale applications en_US
dc.subject Real time monitoring en_US
dc.subject Sensor technologies en_US
dc.subject Internet of things en_US
dc.title A scalable semantic framework for IoT healthcare applications en_US
dc.type Article en_US
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