A Highly Configurable Flexible Analytic Model for MCI/Frailty Risk Detection in Age-friendly Cities

Abstract

The global human population is aging rapidly, however, living longer does not necessarily mean living healthy, active and independent life. The emerging disruptive technologies like the Internet of Things (IoT) are proving instrumental in addressing this prominent societal challenge. Urban IoT infrastructures, designed to support the Smart City vision, enable capturing of personal data for analyzing behaviour of elderly people. Activities within the Horizon 2020 City4Age project are aimed at showing that behavioral analysis can help detect and mitigate risks of Mild Cognitive Impairment (MCI) and frailty problems of elderly. This paper presents the latest developments in extending the configurability and flexibility of the comprehensive City4Age computational model for risk detection. The proposed model extensions have demonstrated seamless adaptation to specific characteristics of various urban contexts, as well as seamless "pluggable" integration of various combined evolving and extendable parameterized algorithm implementations and methods for behaviour variation and risk recognition, based on relevant statistical and machine learning techniques.