1. Data driven MCI and frailty prevention: Geriatric modelling in the City4Age project
- Author
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Sergio Copelli, Letizia Venturini, Giuseppe Nicolardi, Giovanni Ricevuti, and Franco Mercalli
- Subjects
Population ageing ,Engineering ,business.industry ,media_common.quotation_subject ,Scientific literature ,computer.software_genre ,Data science ,Bridge (nautical) ,Data-driven ,Variety (cybernetics) ,Work (electrical) ,Quality (business) ,Data mining ,Cognitive impairment ,business ,computer ,media_common - 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.
- Published
- 2017