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Proceedings Paper

A linear discriminative analysis based fall motion detector using radar
Author(s): Sivan Zlotnikov; Patrick Somaru; Panos P. Markopoulos; Fauzia Ahmad
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Paper Abstract

Remote activity monitoring can support aging-in-place for the elderly, providing crucial capabilities such as fall detection. Falls are the leading cause of accidental death in people aged 65 and over in the United States. The chances of survival are high with low impact on quality of life when prompt assistance is provided after a fall. Radar is at the forefront of research on non-wearable technologies for fall detection and monitoring of activities of daily living for eldercare. Various features extracted from Doppler motion signatures have been proposed in the literature for radar-based fall detection. However, none of these features were specifically designed to provide the most discrimination between the fall and non-fall motion classes. In this paper, we perform linear discriminant analysis (LDA) of Doppler signatures as a first step towards identification of the most discriminative features. LDA performance is evaluated using real data measurements of various indoor human activities and compared with that of existing radar-based fall detection schemes.

Paper Details

Date Published: 14 May 2018
PDF: 6 pages
Proc. SPIE 10658, Compressive Sensing VII: From Diverse Modalities to Big Data Analytics, 106580D (14 May 2018); doi: 10.1117/12.2311574
Show Author Affiliations
Sivan Zlotnikov, Temple Univ. (United States)
Patrick Somaru, Temple Univ. (United States)
Panos P. Markopoulos, Rochester Institute of Technology (United States)
Fauzia Ahmad, Temple 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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