A Three-Stage Low-Complexity Human Fall Detection Method Using IR-UWB Radar

This paper proposes a novel three-stage low-complexity human fall detection method using an impulse radio ultra-wideband (IR-UWB) radar. The core idea lies in the three cascaded stages, namely large-motion detection, rough fall detection and enhanced fall detection. For the large-motion detection, we assume the fall is a very sparse event in daily life and achieve this by a checking of the high Doppler frequency energy. For the rough fall detection, we do not intuitively determine the fall events, but propose six time-frequency features and two position features, and use the support vector data description (SVDD) detector to divide the large-motions into non-fall and fall-like events. For the enhanced fall detection, we add a new feature and use a Mahalanobis distance classifier to finally determine whether a fall happened. The reasons that two classifiers cascaded instead of one classifier are that (1) we can reduce the difficulty of identifying falls directly from daily events by using a large number of non-fall samples to train the SVDD model for anomaly detection, and allowing a certain false alarm rate; and (2) we can achieve a higher fall detection accuracy in a much smaller searching space by identifying falls only from the fall-like events. Additionally, a real-time edge fall detection system with a commonly used micro control unit is developed. Experiment results show that the proposed method exhibits a low computational complexity, and a relative robustness and high fall detection accuracy under a low false alarm rate.