SIMFALL: A Data Generator for RF-Based Fall Detection

Abstract

Fall detection using Radio Frequency (RF) signals with deep learning has exhibited significant promise in recent years. However, the costly collection of RF data with falls has hampered the performance of existing methods. While there has been approaches which can generate RF signals using various simulation methods, they rely on human-body modeling based on other modalities. Moreover, the realism of the generated signals is insufficient because these approaches cannot accurately capture the human radar cross section (RCS). In this paper, we propose SimFall, which generates simulated data for RF-based fall detection without overhead for data collection. SimFall first simulates the fall process by manipulating the human body mesh based on practical fall model. Then a grid shooting and bouncing ray (SBR) method is utilized to calculate the accurate RCS. Finally, SimFall computes the original signal and transforms it into different forms that reveal the features of falls. The experimental results demonstrate that the data produced by SimFall effectively enhances the accuracy of the RF-based fall detection network.