Sequence Learning with Passive RFID Sensors for Real-Time Bed-Egress Recognition in Older People.
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 toward 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 includes a novel sequence learning classifier with a set of features derived from 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.
Asanga
A
Wickramasinghe
Damith C
DC
Ranasinghe
Christophe
C
Fumeaux
Keith D
KD
Hill
Renuka
R
Visvanathan
D000058:Accidental Falls / Q000517:prevention & control*; D000368:Aged; D000369:Aged, 80 and over; D001513:Beds; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D057233:Radio Frequency Identification Device; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine