Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/100707
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Effectiveness of a batteryless and wireless wearable sensor system for identifying bed and chair exits in healthy older people
Author: Shinmoto Torres, R.
Visvanathan, R.
Hoskins, S.
Van den Hengel, A.
Ranasinghe, D.
Citation: Sensors, 2016; 16(4):546-1-546-17
Publisher: MDPI
Issue Date: 2016
ISSN: 1424-8239
1424-8220
Statement of
Responsibility: 
Roberto Luis Shinmoto Torres, Renuka Visvanathan, Stephen Hoskins, Anton van den Hengel and Damith C. Ranasinghe
Abstract: Aging populations are increasing worldwide and strategies to minimize the impact of falls on older people need to be examined. Falls in hospitals are common and current hospital technological implementations use localized sensors on beds and chairs to alert caregivers of unsupervised patient ambulations; however, such systems have high false alarm rates. We investigate the recognition of bed and chair exits in real-time using a wireless wearable sensor worn by healthy older volunteers. Fourteen healthy older participants joined in supervised trials. They wore a batteryless, lightweight and wireless sensor over their attire and performed a set of broadly scripted activities. We developed a movement monitoring approach for the recognition of bed and chair exits based on a machine learning activity predictor. We investigated the effectiveness of our approach in generating bed and chair exit alerts in two possible clinical deployments (Room 1 and Room 2). The system obtained recall results above 93% (Room 2) and 94% (Room 1) for bed and chair exits, respectively. Precision was >78% and 67%, respectively, while F-score was >84% and 77% for bed and chair exits, respectively. This system has potential for real-time monitoring but further research in the final target population of older people is necessary.
Keywords: Fall prevention; bed exits; chair exits; weighted conditional random fields; older people
Rights: © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
RMID: 0030046873
DOI: 10.3390/s16040546
Grant ID: http://purl.org/au-research/grants/arc/DP130104614
Appears in Collections:Computer Science publications

Files in This Item:
File Description SizeFormat 
hdl_100707.pdfPublished version1.88 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.