Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/104871
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Type: Journal article
Title: Sequence learning with passive RFID sensors for real time bed-egress recognition in older people
Author: Wickramasinghe, A.
Ranasinghe, D.
Fumeaux, C.
Hill, K.
Visvanathan, R.
Citation: IEEE Journal of Biomedical and Health Informatics, 2016; PP(99):1-12
Publisher: J-BHI
Issue Date: 2016
ISSN: 2168-2194
2168-2208
Statement of
Responsibility: 
Asanga Wickramasinghe, Damith C Ranasinghe, Christophe Fumeaux, Keith D Hill and Renuka Visvanathan
Abstract: Getting out of bed and ambulating without supervision is identified as one of the major causes of patient falls in hospitals and nursing homes. Therefore, increased supervision is proposed as a key strategy towards falls prevention. An emerging generation of batteryless, lightweight and wearable sensors are creating new possibilities for ambulatory monitoring, where the unobtrusive nature of such sensors makes them particularly adapted for monitoring older people. In this study, we investigate the use of a batteryless Radio Frequency Identification (RFID) tag response to analyze bed-egress movements. We propose a bed-egress movement detection framework that include a novel sequence learning classifier with a set of features derived based on bed-egress motion analysis. We analyzed data from 14 healthy older people (66-86 years old) who wore a wearable embodiment of a batteryless accelerometer integrated RFID sensor platform loosely attached over their clothes at sternum level, and undertook a series of activities including bed-egress in two clinical room settings. The promising results indicate the efficacy of our batteryless bed-egress monitoring framework.
Keywords: Batteryless wearable sensor; bed-egress analysis; sequence learning; older people; support vector machines
Rights: (c) 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information
RMID: 0030066630
DOI: 10.1109/JBHI.2016.2576285
Grant ID: http://purl.org/au-research/grants/arc/DP160103039
Appears in Collections:Electrical and Electronic Engineering publications

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