@inproceedings{9b80d4f697674579a0af8f2a758f9b2d,
title = "A smart ward with a fall detection system",
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.",
keywords = "Deep learnrning, Fall detection, Kinect, Neural networks, Ward management",
author = "Su, {Mu Chun} and Liao, {Jia Wei} and Wang, {Pa Chun} and Wang, {Chen Hsu}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 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 date: 06-06-2017 Through 09-06-2017",
year = "2017",
month = jul,
day = "12",
doi = "10.1109/EEEIC.2017.7977515",
language = "???core.languages.en_GB???",
series = "Conference 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",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Conference 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",
}