An IoT-Aware Approach for Elderly-Friendly Cities

• Published in: IEEE access Vol. 6; pp. 7941 - 7957
• Main Authors: Mulero, Ruben, Almeida, Aitor, Azkune, Gorka, Abril-Jimenez, Patricia, Arredondo Waldmeyer, Maria Teresa, Paramo Castrillo, Miguel, Patrono, Luigi, Rametta, Piercosimo, Sergi, Ilaria
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aging; Algorithms; Data collection; Data management; Elderly; Europe; Internet of Things; IoT; Life expectancy; LOD; MCI; Middleware; Monitoring; monitoring systems; Older people; Risk assessment; risk detection; Senior citizens; Sensors; Smart cities; smart environments
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2800161

The ever-growing life expectancy of people requires the adoption of proper solutions for addressing the particular needs of elderly people in a sustainable way, both from service provision and economic point of view. Mild cognitive impairments and frailty are typical examples of elderly conditions which, if not timely addressed, can turn out into more complex diseases that are harder and costlier to treat. Information and communication technologies, and in particular Internet of Things technologies, can foster the creation of monitoring and intervention systems, both on an ambient-assisted living and smart city scope, for early detecting behavioral changes in elderly people. This allows to timely detect any potential risky situation and properly intervene, with benefits in terms of treatment's costs. In this context, as part of the H2020-funded City4Age project, this paper presents the data capturing and data management layers of the whole City4Age platform. In particular, this paper deals with an unobtrusive data gathering system implementation to collect data about daily activities of elderly people, and with the implementation of the related linked open data (LOD)-based data management system. The collected data are then used by other layers of the platform to perform risk detection algorithms and generate the proper customized interventions. Through the validation of some use-cases, it is demonstrated how this scalable approach, also characterized by unobtrusive and low-cost sensing technologies, can produce data with a high level of abstraction useful to define a risk profile of each elderly person.