Citation

Baig MM, GholamHosseini H, Connolly MJ. Aging Clin. Exp. Res. 2016; 28(6): 1159-1168.

Affiliation

Freemasons' Professor of Geriatric Medicine, University of Auckland, North Shore Hospital, Takapuna, Auckland, New Zealand.

Copyright

(Copyright © 2016, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s40520-015-0510-5

PMID

26786585

Abstract

Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way healthcare is currently delivered. Currently hospital falls are a major healthcare concern worldwide because of the ageing population. Current observational data and vital signs give the critical information related to the patient's physiology, and motion data provide an additional tool in falls risk assessment. These data combined with the patient's medical history potentially may give the interpretation model high information accessibility to predict falls risk. This study aims to develop a robust falls risk assessment system, in order to avoid falls and its related long-term disabilities in hospitals especially among older adults. The proposed system employs real-time vital signs, motion data, falls history and other clinical information. The falls risk assessment model has been tested and evaluated with 30 patients. The results of the proposed system have been compared with and evaluated against the hospital's falls scoring scale.

Language: en