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Realization of radar-based fall detection using spectrograms
Author(s): Baris Erol; Mark Francisco; Arun Ravisankar; Moeness Amin
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Paper Abstract

Radar has emerged as a leading technology supporting large sectors of commerce, defense and security. Enabled by the advent of small, low-cost solid-state and software-defined radar technologies, new radar applications involving cognitive radar, medical and biometric radar, passive radar, and automotive radar have been made possible. In this paper, we examine redundancy in human motion signatures along the data and short-time Fourier transform (STFT) parameters. With an "eye" on a final product, we evaluate the effect of reduced sampling along slow-time on classification performance. The goal is to determine the degree of data down-sampling that can be tolerated without compromising feature extraction or significantly impeding motion classifications. We search for the optimum STFT parameters that provide the best classification performance for the given radar measurements and gain an understanding of their respective nominal range values.

Paper Details

Date Published: 14 May 2018
PDF: 12 pages
Proc. SPIE 10658, Compressive Sensing VII: From Diverse Modalities to Big Data Analytics, 106580B (14 May 2018); doi: 10.1117/12.2309817
Show Author Affiliations
Baris Erol, Villanova Univ. (United States)
Mark Francisco, Comcast Corp. (United States)
Arun Ravisankar, Comcast Corp. (United States)
Moeness Amin, Villanova Univ. (United States)


Published in SPIE Proceedings Vol. 10658:
Compressive Sensing VII: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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