Journal
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
Volume 97, Issue -, Pages 598-619Publisher
ELSEVIER
DOI: 10.1016/j.future.2019.03.019
Keywords
Ambient assisted living; BLE; Internet of things; Big data; Data analytics; Performance
Categories
Funding
- European Union's Horizon 2020 research and innovation programme [City4Age project] [689731]
- H2020 Societal Challenges Programme [689731] Funding Source: H2020 Societal Challenges Programme
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A growing number of elderly people (65+ years old) are affected by particular conditions, such as Mild Cognitive Impairment (MCI) and frailty, which are characterized by a gradual cognitive and physical decline. Early symptoms may spread across years and often they are noticed only at late stages, when the outcomes remain irrevocable and require costly intervention plans. Therefore, the clinical utility of early detecting these conditions is of substantial importance in order to avoid hospitalization and lessen the socio-economic costs of caring, while it may also significantly improve elderly people's quality of life. This work deals with a critical performance analysis of an Internet of Things aware Ambient Assisted Living (AAL) system for elderly monitoring. The analysis is focused on three main system components: (i) the City-wide data capturing layer, (ii) the Cloud-based centralized data management repository, and (iii) the risk analysis and prediction module. Each module can provide different operating modes, therefore the critical analysis aims at defining which are the best solutions according to context's needs. The proposed system architecture is used by the H2020 City4Age project to support geriatricians for the early detection of MCI and frailty conditions. (C) 2019 The Authors. Published by Elsevier B.V.
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