Collaborative Internet of Things (C-IoT) Data Analysis for Enhancing Activity Recognition and Preventing Serious Health Problems
| Autor: | Imad Belkacem, Aymen Gammoudi, Nasredine Cheniki, Yacine Sam, Nizar Messai |
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| Přispěvatelé: | Bases de données et traitement des langues naturelles (BDTLN), Laboratoire d'Informatique Fondamentale et Appliquée de Tours (LIFAT), Université de Tours-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université de Tours-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Messai, Nizar, Université de Tours (UT)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université de Tours (UT)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | 30th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises WETICE 2021 30th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises WETICE 2021, Oct 2021, Bayonne (en ligne), France |
| Popis: | International audience; Collaborative Internet of Things (C-IoT) is an emerging paradigm that has generated a large amount of accessible and usable data in real-time, constituting an interesting tool for decision-making problems. The paper presents a healthcare data analytics approach based on collecting and analyzing data from C-IoT. We first provide an architectural plan involving several distributed sites collaborating to collect necessary data for patient's health conditions monitoring. We use for that wellknown IoT technologies like Radio Frequency IDentification (RFID), Near Field Communication(NFC), Beacons and Ambient Assisted Living sensors. We then learn on such data to create adequate patient profiles and use the Pearson Correlation Coefficient (PCC) over a long period of time data to detect potential health risks. Finally, decisions are made regarding the patient's condition according to the symptoms/diseases detected. |
| Databáze: | OpenAIRE |
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