Next Article in Journal
A Fiber Bragg Grating Sensing-Based Micro-Vibration Sensor and Its Application
Previous Article in Journal
Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(4), 546; doi:10.3390/s16040546

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

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.
Academic Editor: Panicos Kyriacou
Received: 9 October 2015 / Revised: 17 March 2016 / Accepted: 6 April 2016 / Published: 15 April 2016
View Full-Text   |   Download PDF [1749 KB, uploaded 15 April 2016]   |  

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. View Full-Text
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
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top