@article{ref1,
title="A multifactorial falls risk prediction model for hospitalized older adults",
journal="Conference proceedings - IEEE engineering in medicine and biology society",
year="2014",
author="GholamHosseini, Hamid and Baig, Mirza Mansoor and Connolly, Martin J. and Linden, Maria",
volume="2014",
number="",
pages="3484-3487",
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.<p /> <p>Language: en</p>",
language="en",
issn="1557-170X",
doi="10.1109/EMBC.2014.6944373",
url="http://dx.doi.org/10.1109/EMBC.2014.6944373"
}