Citation

GholamHosseini H, Baig MM, Connolly MJ, Linden M. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2014; 2014: 3484-3487.

Copyright

(Copyright © 2014, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/EMBC.2014.6944373

PMID

25570741

Abstract

Ageing population worldwide has grown fast with more cases of chronic illnesses and co-morbidity, involving higher healthcare costs. Falls are one of the leading causes of unintentional injury-related deaths in older adults. The aim of this study was to develop a robust multifactorial model toward the falls risk prediction. The proposed model employs real-time vital signs, motion data, falls history and muscle strength. Moreover, it identifies high-risk individuals for the development falls in their activity of daily living (ADL). The falls risk prediction model has been tested at a controlled-environment in hospital with 30 patients and compared with the results from the Morse fall scale. The simulated results show the proposed algorithm achieved an accuracy of 98%, sensitivity of 96% and specificity of 100% among a total of 80 intentional falls and 40 ADLs. The ultimate aim of this study is to extend the application to elderly home care and monitoring.

Language: en