TY  - JOUR
PY  - 2023//
TI  - An unobtrusive fall detection system using ceiling-mounted ultra-wideband radar
JO  - Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
A1  - Lu, Wei
A1  - Kumar, Saurav
A1  - Sandhu, Moid
A1  - Zhang, Qing
SP  - 1
EP  - 5
VL  - 2023
IS  - 
N2  - 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>
LA  - en
SN  - 2375-7477
UR  - http://dx.doi.org/10.1109/EMBC40787.2023.10341081
ID  - ref1
ER  -