Light Weight Passive Human Motion Detection with WiFi

Abstract

WiFi-based passive human motion detection has attracted extensive attention due to its primary and fundamental function to boost applications in practice. Compared with traditional Receive Signal Strength Indicator, Channel State Information contains more fine grained information in the form of subcarrier. Most previous works focus on the joint indicators or complicated algorithm for better system performance, and treat all the subcarriers nondiscriminatory. However, the subcarrier suffer different effects in complex indoor environment, the undifferentiated approach might increase the complexity of the system and reduce the accuracy. To this content, we propose a light weight and effective human motion detection system from a novel perspective of sensitivity and similarity based subcarrier selection. The experiment results of an open dataset and real scenarios indicate that the proposed system is accurate and robust.