Citation

Lu W, Kumar S, Sandhu M, Zhang Q. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2023; 2023: 1-5.

Copyright

(Copyright © 2023, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/EMBC40787.2023.10341081

PMID

38083299

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%.

Language: en