A critical analysis of an IoT—aware AAL system for elderly monitoring
Future generations computer systems
h i g h l i g h t s • Unobtrusive systems are useful for monitoring elderly behaviour and detect changes. • Wearable devices for BLE indoor positioning and body motility are energy greedy. • Digest mode for data sending to the Shared Repository is the most preferable way. • Linked Open Data to share results is fundamental in a Smart City perspective. • Frailty/MCI risk detection based on high-level geriatric (sub-)factors is effective. a b s t r a c t A growing number of elderly people (65+
... s 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.