NDLI: Automatic fall detection based on Doppler radar motion signature

Content Provider	IEEE Xplore Digital Library
Author	Liang Liu Popescu, M. Skubic, M. Rantz, M. Yardibi, T. Cuddihy, P.
Copyright Year	2011
Description	Author affiliation: GE Global Research, Schenectady, NY, USA (Yardibi, T.; Cuddihy, P.) \|\| ECE Department of Missouri, Columbia, MO, USA (Liang Liu; Popescu, M.; Skubic, M.; Rantz, M.)
Abstract	Falling is a common health problem for elderly. It is reported that more than one third of adults 65 and older fall each year in the United States. To address the problem, we are currently developing a Doppler radar-based fall detection system. Doppler radar sensors provide an inexpensive way to recognize human activity. In this paper, we employed mel-frequency cepstral coefficients (MFCC) to represent the Doppler signatures of various human activities such as walking, bending down, falling, etc. Then we used two different classifiers, SVM and kNN, to automatically detect falls based on the extracted MFCC features. We obtained encouraging classification results on a pilot dataset that contained 109 falls and 341 non-fall human activities.
Starting Page	222
Ending Page	225
File Size	2297731
Page Count	4
File Format	PDF
ISBN	9781612847672
e-ISBN	9781936968152
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-23
Publisher Place	Ireland
Access Restriction	Subscribed
Rights Holder	ICST
Subject Keyword	Humans fall detection radar classification eldercare Feature extraction kNN Doppler radar Sensors SVM Doppler effect Spectrogram MFCC features
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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