FRID: Indoor Fine-Grained Real-Time Passive Human Motion Detection

With the eruptible popularity of wireless sensing, wireless device-free passive human detection has received widespread attention. Indoor fine-grained device-free passive human motion detection based on the PHY layer information is rapidly developed. Since the received signal features can vary under different multipath propagation conditions, in the paper, we propose a lightweight and real-time passive human motion via physical layer phase information, which is independent of the indoor scenarios and needs no re-calibration. We firstly obtain available phase feature by a linear transformation on the raw channel state information(CSI). The real-time human motion detection is implemented based on two developed schemes: short-term averaged variance ratio (SVR) and long-term averaged variance ratio (LVR). We realize the design with commercial WiFi devices and evaluate it in typical multipath-rich indoor scenarios. As demonstrated in the experiments, our approach can achieve high detection rate and low false positive rate.