Fall Detection and Classifications Based on Time-Scale strongRadarstrong.pdfVIP

Fall Detection and Classifications Based on Time-Scale Radar Signal Characteristics Ajay Gadde, Moeness G. Amin, Yimin D. Zhang*, Fauzia Ahmad Center for Advanced Communications Villanova University, Villanova, PA 19085 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. Keywords: Fall detection, assisted living, Doppler signature, wavelet transform, non-stationary signal. 1. INTRODUCTION One of the major public health problems is elderly falls. Prompt assistance after a fall can reduce complications and save lives. Therefore, it is very important to detect a fall immediately as it happens and mobilize first responders for proper care and attendance to possible injury. In recent years, many fall detection systems have been proposed in the literature. These can be categorized into two main types of fall monitoring devices, namely, wearable and non-wearable. The simplest