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Proceedings Paper

Fall detection and classifications based on time-scale radar signal characteristics
Author(s): Ajay Gadde; Moeness G. Amin; Yimin D. Zhang; Fauzia Ahmad
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Paper Abstract

Unattended catastrophic falls result in risk to the lives of elderly. There are growing efforts and rising interest in detecting falls of the aging population, especially those living alone. Radar serves as an effective non-intrusive sensor for detecting human activities. For radar to be effective, it is important to achieve low false alarms, i.e., the system can reliably differentiate between a fall and other human activities. In this paper, we discuss the time-scale based signal analysis of the radar returns from a human target. Reliable features are extracted from the scalogram and are used for fall classifications. The classification results and the advantages of using a wavelet transform are discussed.

Paper Details

Date Published: 29 May 2014
PDF: 9 pages
Proc. SPIE 9077, Radar Sensor Technology XVIII, 907712 (29 May 2014); doi: 10.1117/12.2050998
Show Author Affiliations
Ajay Gadde, Villanova Univ. (United States)
Moeness G. Amin, Villanova Univ. (United States)
Yimin D. Zhang, Villanova Univ. (United States)
Fauzia Ahmad, Villanova Univ. (United States)

Published in SPIE Proceedings Vol. 9077:
Radar Sensor Technology XVIII
Kenneth I. Ranney; Armin Doerry, Editor(s)

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