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AuthorErol, Baris
AuthorAmin, Moeness
AuthorAhmad, Fauzia
AuthorBoashash, B.
Available date2021-09-05T05:40:16Z
Publication Date2016
Publication NameProceedings of SPIE - The International Society for Optical Engineering
ResourceScopus
ISSN0277786X
URIhttp://dx.doi.org/10.1117/12.2224984
URIhttp://hdl.handle.net/10576/22708
AbstractFalls are a major cause of accidents in elderly people. Even simple falls can lead to severe injuries, and sometimes result in death. Doppler fall detection has drawn much attention in recent years. Micro-Doppler signatures play an important role for the Doppler-based radar systems. Numerous studies have demonstrated the offerings of micro-Doppler characteristics for fall detection. In this respect, a plethora of micro-Doppler signature features have been proposed, including those stemming from speech recognition and wavelet decomposition. In this work, we consider four different sets of features for fall detection. These can be categorized as spectrogram based features, wavelet based features, mel-frequency cepstrum coefficients, and power burst curve features. Support vector machine is employed as the classifier. Performance of the respective fall detectors is investigated using real data obtained with the same radar operating resources and under identical sensing conditions. For the considered data, the spectrogram based feature set is shown to provide superior fall detection performance. 2016 SPIE.
Languageen
PublisherSPIE
SubjectCepstrum
Fall detection
Micro-Doppler signatures
Support vector machine
Wavelets
TitleRadar fall detectors: A comparison
TypeConference Paper
Volume Number9829
dc.accessType Abstract Only


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