Radar-based elderly fall detection using SPWVD and ResNet Network

Abstract Falls in the elderly population present substantial health risks, often resulting in morbidity and a decline in quality of life. Conventional fall detection methods, including wearable devices and cameras, are hindered by issues such as variable lighting conditions and privacy considerations. Radarbased fall detection has emerged as a promising alternative, providing an unobtrusive method. This study aims to classify fall detection using Smoothed Pseudo Wigner-Ville distribution (SPWVD) images and ResNet-50 model. For this, an online publicly available radar database comprising 15 subjects is utilized. Radar signals is processed into SPWVD timefrequency representation images for analysis. The SPWVD images is fed into the ResNet-50 model. Experiments are performed and performance is evaluated using 10- fold cross validation. The proposed approach is able to distinguish elderly fall. Using ResNet-50 model, the approach yields a maximum average classification f-measure, area under curve score, precision and, sensitivity of 67.15%, 52.31%, 50.55% and, 99.88%, respectively. Hence, the proposed framework holds promise for accurately and efficiently detecting falls among the elderly population within their private environments.