Human Fall Detection Method Beyond the Sensing Range of IR-UWB Using Ambient Vibration

Fall detection is crucial in elderly healthcare, traditionally relying on wearable devices for sensing. To avoid the burden and discomfort caused by wearable devices, contact-free methods using radio frequency (RF) signals have emerged as a promising alternative due to their ubiquity and non-invasiveness. Existing contact-free methods predominantly extract physiological motion features, e.g., the speed and acceleration of human falls from RF signals and analyze the motion time series to determine if a fall event has occurred. While current methods achieve high detection accuracy in line-of-sight (LoS) scenarios, they still face limitations in sensing range and non-light-of-sight (NLoS). To address this, we propose a method using the commercial off-the-shelf (COTS) impulse radio-ultra wideband (IR-UWB) devices to detect human falls beyond the sensing range. The key insight of our method is identifying ambient vibration features induced by falls outside the sensing range to determine fall events. By developing the IQ entropy and farthest points-pair distance algorithm, signal features from subtle vibrations caused by falls are extracted. We implement a UWFall prototype system built on COTS IR-UWB devices and conduct extensive experimental evaluations under various environments. The results demonstrate that UWFall system achieves a recognition accuracy over 90% with one single transceiver. Furthermore, the detection accuracy for falls beyond 7 meters exceeds 86%, and this system maintains high robustness in NLoS scenarios.