Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People.
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.
Auto-ID Lab, The University of Adelaide, North Terrace, Adelaide SA 5005, Australia. roberto.shinmototorres@adelaide.edu.au.
Roberto Luis Shinmoto
RL
Torres
Aged & Extended Care Services, The Queen Elizabeth Hospital, Woodville South SA 5011, Australia. renuka.visvanathan@adelaide.edu.au.|Adelaide Geriatrics Training and Research with Aged Care (GTRAC) Centre, The University of Adelaide, North Terrace, Adelaide SA 5005, Australia. renuka.visvanathan@adelaide.edu.au.
Renuka
R
Visvanathan
Aged & Extended Care Services, The Queen Elizabeth Hospital, Woodville South SA 5011, Australia. Stephen.Hoskins@health.sa.gov.au.
Stephen
S
Hoskins
Australian Centre for Visual Technologies, The University of Adelaide, North Terrace, Adelaide SA 5005, Australia. anton.vandenhengel@adelaide.edu.au.
Anton
A
van den Hengel
Auto-ID Lab, The University of Adelaide, North Terrace, Adelaide SA 5005, Australia. damith.ranasinghe@adelaide.edu.au.
Damith C
DC
Ranasinghe
D000058:Accidental Falls / Q000517:prevention & control*; D000368:Aged; D000369:Aged, 80 and over; D015374:Biosensing Techniques / Q000295:instrumentation / Q000379:methods*; D011211:Electric Power Supplies; D005260:Female; D006761:Hospitals; D006801:Humans; D008297:Male; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000379:methods*; D009068:Movement / Q000502:physiology; D059015:Wireless Technology / Q000295:instrumentation*
bed exits; chair exits; fall prevention; older people; weighted conditional random fields