Towards a Dynamic Fresnel Zone Model to WiFi-based Human Activity Recognition

The passive WiFi sensing research has largely centered on activity sensing using fixed-location WiFi transceivers, leading to the development of several theoretical models that aim to map received WiFi signals to human activity. Of these models, the Fresnel zone model has shown to be particularly noteworthy. However, the growing popularity of mobile WiFi receivers has not been matched by corresponding research on mobile receiver-based theoretical models. This paper fills this gap by presenting the first theoretical model to quantify the impact of moving a moving receiver for WiFi sensing. We propose a novel dynamic Fresnel zone model in the free space of an indoor environment, which takes the form of a cluster of concentric hyperbolas centered on the transmitter and reflection subject. We examine three properties of this model, i.e., relating the variation in RF signals received by the receiver to the position and orientation of the human, the movement of the receiver, and the presence of other objects in the environment. To validate this model, we develop a prototype system and conduct extensive experiments. The results are consistent with our theoretical analysis, and the system is able to detect the direction of the transmitter with an accuracy of 10° or better, measure the receiver's relative motion displacement within 1 cm a millimeter-level accuracy, and classify five receiver-side activities with an accuracy of 98%. Our work moves a significant step forward in WiFi sensing and may potentially open up new avenues for future research.