A scalable semantic framework for IoT healthcare applications

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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.
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
Epidemic detection, Middleware, Ontologies, Simulated activities, Epidemiology, Semantics, Activities of Daily Living, Activity recognition, Complex event processing (CEP), Discrete-event simulators, Health care application, Large-scale applications, Real time monitoring, Sensor technologies, Internet of things
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