This paper presents a step toward the development of a data-centric approach to prevention of Mild Cognitive Impairment and frailty in the elderly population. The scientific literature provides a large number of “indicators” for assessing the quality of behavior for aged individuals, in order to predict possible decaying. On the opposite side, a large variety of sensors and datasets today allows the effective collection of elementary data about actions performed by individuals. This paper proposes to build a bridge between these two sides. In a bottom-up vision, data from sensors and smart cities' datasets are aggregated and interpreted in a way that leads to reliable assessment of the indicators. In a top-down vision, indicators are translated into data analysis. The work described in this paper is part of City4age, a project partially funded by the EU within the H2020 Programme.
Data driven MCI and frailty prevention: Geriatric modelling in the City4Age project
Ricevuti, Giovanni;VENTURINI, LETIZIA;
2017-01-01
Abstract
This paper presents a step toward the development of a data-centric approach to prevention of Mild Cognitive Impairment and frailty in the elderly population. The scientific literature provides a large number of “indicators” for assessing the quality of behavior for aged individuals, in order to predict possible decaying. On the opposite side, a large variety of sensors and datasets today allows the effective collection of elementary data about actions performed by individuals. This paper proposes to build a bridge between these two sides. In a bottom-up vision, data from sensors and smart cities' datasets are aggregated and interpreted in a way that leads to reliable assessment of the indicators. In a top-down vision, indicators are translated into data analysis. The work described in this paper is part of City4age, a project partially funded by the EU within the H2020 Programme.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
