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Context-Aware Early Warning System for In-Home Healthcare Using Internet-of-Things

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Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 169)

Abstract

Early warning score (EWS) is a prediction method to notify caregivers at a hospital about the deterioration of a patient. Deterioration can be identified by detecting abnormalities in patient’s vital signs several hours prior the condition of the patient gets life-threatening. In the existing EWS systems, monitoring of patient’s vital signs and the determining the score is mostly performed in a paper and pen based way. Furthermore, currently it is done solely in a hospital environment. In this paper, we propose to import this system to patients’ home to provide an automated platform which not only monitors patents’ vital signs but also looks over his/her activities and the surrounding environment. Thanks to the Internet-of-Things technology, we present an intelligent early warning method to remotely monitor in-home patients and generate alerts in case of different medical emergencies or radical changes in condition of the patient. We also demonstrate an early warning score analysis system which continuously performs sensing, transferring, and recording vital signs, activity-related data, and environmental parameters.

Keywords

  • Early warning score
  • Internet-of-Things
  • e-Health
  • Remote patient monitoring

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Correspondence to Arman Anzanpour .

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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Anzanpour, A., Rahmani, AM., Liljeberg, P., Tenhunen, H. (2016). Context-Aware Early Warning System for In-Home Healthcare Using Internet-of-Things. In: , et al. Internet of Things. IoT Infrastructures. IoT360 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 169. Springer, Cham. https://doi.org/10.1007/978-3-319-47063-4_56

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  • DOI: https://doi.org/10.1007/978-3-319-47063-4_56

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47062-7

  • Online ISBN: 978-3-319-47063-4

  • eBook Packages: Computer ScienceComputer Science (R0)