TY  - JOUR
PY  - 2014//
TI  - A multifactorial falls risk prediction model for hospitalized older adults
JO  - Conference proceedings - IEEE engineering in medicine and biology society
A1  - GholamHosseini, Hamid
A1  - Baig, Mirza Mansoor
A1  - Connolly, Martin J.
A1  - Linden, Maria
SP  - 3484
EP  - 3487
VL  - 2014
IS  - 
N2  - 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>
LA  - en
SN  - 1557-170X
UR  - http://dx.doi.org/10.1109/EMBC.2014.6944373
ID  - ref1
ER  -