TY - JOUR
T1 - A critical analysis of an IoT—aware AAL system for elderly monitoring
AU - Almeida, Aitor
AU - Mulero, Rubén
AU - Rametta, Piercosimo
AU - Urošević, Vladimir
AU - Andrić, Marina
AU - Patrono, Luigi
N1 - Publisher Copyright:
© 2019
PY - 2019/8
Y1 - 2019/8
N2 - 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.
AB - 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.
KW - Ambient assisted living
KW - BLE
KW - Big data
KW - Data analytics
KW - Internet of things
KW - Performance
UR - https://www.scopus.com/pages/publications/85063231571
U2 - 10.1016/j.future.2019.03.019
DO - 10.1016/j.future.2019.03.019
M3 - Article
AN - SCOPUS:85063231571
SN - 0167-739X
VL - 97
SP - 598
EP - 619
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -