@article{ref1,
title="An unobtrusive fall detection system using ceiling-mounted ultra-wideband radar",
journal="Annual International Conference of the IEEE Engineering in Medicine and Biology Society.",
year="2023",
author="Lu, Wei and Kumar, Saurav and Sandhu, Moid and Zhang, Qing",
volume="2023",
number="",
pages="1-5",
abstract="Falls are among the most devastating events that can happen to an older person. Automatic fall detection systems aim to solve this problem by alerting carers and family the moment a fall occurs. This paper presents the development of an unobtrusive fall detection system using ultra-wideband (UWB) radar. The proposed system employed a ceiling-mounted UWB radar to avoid object occlusion and allow for flexible implementation. An innovative pre-processing method was developed to effectively enhance motion and reduce noise from raw UWB data. We designed a trial protocol composed of common types of falls in older population and activities of daily living (ADL). A fall detection algorithm based on convolutional neural networks was developed with simulated falls and ADLs obtained from ten participants following the trial protocol in a clear and cluttered living environment. The fall detection system achieved an accuracy of 93.97%, with a sensitivity of 95.58% and specificity of 92.68%.<p /> <p>Language: en</p>",
language="en",
issn="2375-7477",
doi="10.1109/EMBC40787.2023.10341081",
url="http://dx.doi.org/10.1109/EMBC40787.2023.10341081"
}