A critical analysis of an IoT—aware AAL system for elderly monitoring

https://doi.org/10.1016/j.future.2019.03.019Get rights and content
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Highlights

  • 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.

Abstract

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.

Keywords

Ambient assisted living
BLE
Internet of things
Big data
Data analytics
Performance

Cited by (0)

Aitor Almeida is a researcher and project manager at the DeustoTech-Internet research group at Deusto Foundation. His research interests include the analysis of the behaviour of the users in intelligent environments and the study of the users’ activity and discourse on social networks. He received a PhD in Computer Science from the University of Deusto.

Rubén Mulero is working as a technical researcher at the DeustoTech-Internet research group at Deusto Foundation. He received his BSc degree in Computer Engineering and Intelligent Systems in 2015 from University of the Basque Country, Spain. Currently he is finishing his MSc on Computational Engineering and Intelligent Systems and preparing his master thesis about reinforcement learning. His research interest includes, web of things, intelligent systems and machine learning.

Piercosimo Rametta received the Master’s degree in Computer Engineering with honours at the University of Salento, Lecce, Italy, in 2013. His thesis concerned the definition and implementation of a novel mash-up tool for Wireless Sensor Networks’ configuration. Since November 2013 he collaborates with IDA Lab — IDentification Automation Laboratory at the Department of Innovation Engineering, University of Salento. His activity is focused on the definition and implementation of new mash-up tools for managing smart environments based on Wireless Sensor Networks and Internet of Things.

Vladimir Urošević received the Master of Arts degree in Industrial Design from the Belgrade University of Arts, Serbia, in 2001. He is the Research & Development Manager of Belit Ltd. Belgrade, a software development and research SME, with almost two decades of experience in development and deployment of large-scale production software systems (in finance, public administration, e-health, energy efficiency, IP management & protection…), and hardware industrial products (medical & therapeutic equipment, lab & field instrumentation, IoT devices, PoS terminals, etc.).

Marina Andrić received Diploma in Mathematics from Faculty of Mathematics of the University of Belgrade, Serbia, in 2011 and PhD in Computer Science from IMT Lucca, Italy, in 2017. She is currently working as a software developer and data analyst in Belit Ltd, Belgrade. Her research interests encompass health care, behavioural and well-being data analytics and process re-engineering and improvement.

Luigi Patrono received his MS in Computer Engineering from University of Lecce, Lecce, Italy, in 1999 and PhD in Innovative Materials and Technologies for Satellite Networks from ISUFI-University of Lecce, Italy, in 2003. He is an Assistant Professor of Computer Networks and Internet of Things at the University of Salento, Lecce, Italy. His research interests include RFID, IoT, wireless sensor networks, and embedded systems. He authored more than 120 scientific papers published in international journals and conferences. He has been Organizing Chair of some international symposia and workshops, technically co-sponsored by the IEEE Communication Society, focused on Internet of Things.