Fall detection and classifications based on time-scale radar signal characteristics

Ajay Gadde; Moeness G. Amin; Yimin D. Zhang; Fauzia Ahmad

doi:10.1117/12.2050998

29 May 2014 Fall detection and classifications based on time-scale radar signal characteristics

Ajay Gadde, Moeness G. Amin, Yimin D. Zhang, Fauzia Ahmad

Proceedings Volume 9077, Radar Sensor Technology XVIII; 907712 (2014) https://doi.org/10.1117/12.2050998
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

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.

Citation Download Citation

Ajay Gadde, Moeness G. Amin, Yimin D. Zhang, and Fauzia Ahmad "Fall detection and classifications based on time-scale radar signal characteristics", Proc. SPIE 9077, Radar Sensor Technology XVIII, 907712 (29 May 2014); https://doi.org/10.1117/12.2050998

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