Abstract
The percentage of seniors in the global population is constantly growing and solutions in the field of fall detection and early detection of neuro-degenerative pathologies have a crucial role in order to increase life expectancy and quality of life. This study aims to extend fall detection and effective recognition of early signs of diseases to new smart environments, conceiving the decentralization of diagnostic monitoring in everyday life activities in a more pervasive paradigm. Inspiring to research outcomes, in this work an architecture is designed to detect falls in crowded indoor environments during events/exhibitions, for favoring a timely and effective intervention. It also foresees a continue monitoring of the gait for seniors during the visit, thus extracting key features which are stored on a dedicated database. The proposed solution allows third party researchers to perform analysis on the obtained gait datasets, through the adoption of advanced data-mining techniques for the detection of early signs of neuro-degenerative diseases and other pathologies. The architecture designed here aims to provide a step forward concerning the extension of smart monitoring environments for the detection of falls and early signs of pathologies in everyday life, in a more pervasive and decentralized paradigm.
Highlights
Aging of population and the percentage of seniors with respect to young people are constantly growing
This work provides an overview about some solutions based on the adoption of wearable devices for accurate fall detection and effective gait features extraction and classification
A hardware and software innovative architecture has been proposed; it envisages a smart environment focused on fall detection and gait monitoring for seniors during indoor events, museum visits, and so on
Summary
Aging of population and the percentage of seniors with respect to young people are constantly growing. The proposed architecture integrates a fall detector and a gait data monitoring system in a single low power IoT device, supported by a pervasive wireless communication system and a specific application software It allows to adopt state-of-the-art machine learning algorithms for fall detection and gait monitoring in different environments, for detecting early signs related to neuro-degenerative disorders, beyond specific or limited clinic tests. This effort aims to provide a step forward on the possibility to extend the monitoring of early signs of neuro-degenerative disorders beyond specific clinic tests in smart environments, to enhance the timely detection of such diseases and to prevent the exacerbation phase.
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