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Volume 16
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10.3390/s16040546
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Article

Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People

by
Roberto Luis Shinmoto Torres
1,*,
Renuka Visvanathan
2,3,
Stephen Hoskins
2,
Anton Van den Hengel
4 and
Damith C. Ranasinghe
1
1
Auto-ID Lab, The University of Adelaide, North Terrace, Adelaide SA 5005, Australia
2
Aged & Extended Care Services, The Queen Elizabeth Hospital, Woodville South SA 5011, Australia
3
Adelaide Geriatrics Training and Research with Aged Care (GTRAC) Centre, The University of Adelaide, North Terrace, Adelaide SA 5005, Australia
4
Australian Centre for Visual Technologies, The University of Adelaide, North Terrace, Adelaide SA 5005, Australia
*
Author to whom correspondence should be addressed.
Sensors 2016, 16(4), 546; https://doi.org/10.3390/s16040546
Submission received: 9 October 2015 / Revised: 17 March 2016 / Accepted: 6 April 2016 / Published: 15 April 2016
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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 fall prevention; bed exits; chair exits; weighted conditional random fields; older people
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MDPI and ACS Style

Shinmoto Torres, R.L.; Visvanathan, R.; Hoskins, S.; Van den Hengel, A.; Ranasinghe, D.C. Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People. Sensors 2016, 16, 546. https://doi.org/10.3390/s16040546

AMA Style

Shinmoto Torres RL, Visvanathan R, Hoskins S, Van den Hengel A, Ranasinghe DC. Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People. Sensors. 2016; 16(4):546. https://doi.org/10.3390/s16040546

Chicago/Turabian Style

Shinmoto Torres, Roberto Luis, Renuka Visvanathan, Stephen Hoskins, Anton Van den Hengel, and Damith C. Ranasinghe. 2016. "Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People" Sensors 16, no. 4: 546. https://doi.org/10.3390/s16040546

APA Style

Shinmoto Torres, R. L., Visvanathan, R., Hoskins, S., Van den Hengel, A., & Ranasinghe, D. C. (2016). Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People. Sensors, 16(4), 546. https://doi.org/10.3390/s16040546

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