TY - JOUR
T1 - An IoT-aware approach for elderly-friendly cities
AU - Mulero, Rubén
AU - Aitor, Almeida
AU - Gorka, Azkune
AU - Abril-Jiménez, Patricia
AU - Waldmeyer, Maria Teresa Arredondo
AU - Castrillo, Miguel Páramo
AU - Patrono, Luigi
AU - Rametta, Piercosimo
AU - Sergi, Ilaria
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Elderly
KW - IoT
KW - LOD
KW - MCI
KW - Monitoring systems
KW - Risk detection
KW - Smart environments
UR - https://www.scopus.com/pages/publications/85041377310
U2 - 10.1109/ACCESS.2018.2800161
DO - 10.1109/ACCESS.2018.2800161
M3 - Article
AN - SCOPUS:85041377310
SN - 2169-3536
VL - 6
SP - 7941
EP - 7957
JO - IEEE Access
JF - IEEE Access
ER -
