Faculty of Engineering, Applied Science and Technology

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http://hdl.handle.net/20.500.12519/564

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Browsing Faculty of Engineering, Applied Science and Technology by Subject "Activities of Daily Living"
  • Item
    Deepkover - an adaptive artful intelligent assistance system for cognitively impaired people
    (Taylor and Francis Ltd., 2010) Najjar, Mehdi ; Courtemanche, Francois ; Hamam, Habib ; Mayers, Andre
    This article presents a novel modular adaptive artful intelligent assistance system for cognitively and/or memory impaired people engaged in the realisation of their activities of daily living (ADLs). The goal of this assistance system is to help disabled persons moving/evolving within a controlled environment in order to provide logistic support in achieving their ADLs. Empirical results of practical tests are presented and interpreted. Some deductions about the key features that represent originalities of the assistance system are drawn and future works are announced. © 2010 Taylor & Francis Group, LLC.
  • Item
    A scalable semantic framework for IoT healthcare applications
    (Springer, 2020) Zgheib, Rita ; Kristiansen, Stein ; Conchon, Emmanuel ; Plageman, Thomas ; Goebel, Vera ; Bastide, Rémi
    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.