Citation

Kostopoulos P, Kyritsis AI, Deriaz M, Konstantas D. Procedia Comput. Sci. 2016; 98: 212-219.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.procs.2016.09.035

PMID

unavailable

Abstract

Falls among older people remain a very important public healthcare issue. In the majority of fall events external support is imperative in order to avoid major consequences. Therefore, the ability to automatically detect these fall events could help reducing the response time and significantly improve the prognosis of fall victims. This paper presents a practical real time fall detection system running on a smartwatch (F2D). A decision module takes into account the rebound after the fall and the residual movement of the user, matching a detected fall pattern to an actual fall. The last module of F2D is the location module which makes our system very useful for nursing homes that host elderly people. The fall detection algorithm has been tested by Fondation Suisse pour les Téléthèses (FST), the project partner who is responsible for the commercialization of our system. By testing with real data and achieving an accuracy of 96.01% we have a fall detection system ready to be deployed on the market and by adding the location module we can provide it to nursing homes for elderly people.

Language: en