A smart ward with a fall detection system

Mu Chun Su, Jia Wei Liao, Pa Chun Wang, Chen Hsu Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Fall incidents are one of the most important ward safety issues in hospitals because falls may cause extreme injuries, resulting in serious physical, psychological, and social consequences. In this paper, we present a smart ward with a fall detection system. The smart ward can issue an alarm signal to corresponding healthcare givers once it detects a fall event. The smart ward is consisted of a Kinect depth camera and a neural-network-based fall detection algorithm. We deigned six scenarios to test the performance of the proposed smart ward. The accuracy ratio was 98.15% and the KAPPA value was 0.96.

Original languageEnglish
Title of host publicationConference Proceedings - 2017 17th IEEE International Conference on Environment and Electrical Engineering and 2017 1st IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538639160
DOIs
StatePublished - 12 Jul 2017
Event17th IEEE International Conference on Environment and Electrical Engineering and 2017 1st IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2017 - Milan, Italy
Duration: 6 Jun 20179 Jun 2017

Publication series

NameConference Proceedings - 2017 17th IEEE International Conference on Environment and Electrical Engineering and 2017 1st IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2017

Conference

Conference17th IEEE International Conference on Environment and Electrical Engineering and 2017 1st IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2017
Country/TerritoryItaly
CityMilan
Period6/06/179/06/17

Keywords

  • Deep learnrning
  • Fall detection
  • Kinect
  • Neural networks
  • Ward management

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