Radar fall detection using principal component analysis

Branka Jokanovic; Moeness Amin; Fauzia Ahmad; Boualem Boashash

doi:10.1117/12.2225106

12 May 2016 Radar fall detection using principal component analysis

Branka Jokanovic, Moeness Amin, Fauzia Ahmad, Boualem Boashash

Proceedings Volume 9829, Radar Sensor Technology XX; 982919 (2016) https://doi.org/10.1117/12.2225106
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

Citation Download Citation

Branka Jokanovic, Moeness Amin, Fauzia Ahmad, and Boualem Boashash "Radar fall detection using principal component analysis", Proc. SPIE 9829, Radar Sensor Technology XX, 982919 (12 May 2016); https://doi.org/10.1117/12.2225106

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