Recently, with the popularity of ubiquitous computing, intelligent sensing technology has become the focus of researchers, and non-contact sensing based on WiFi is more and more popular in academia and industry because of its excellent generality, low deployment cost, and great user experience. The typical non-contact sensing work based on WiFi includes gesture recognition, breath detection, intrusion detection, behavior recognition, etc. For real-life deployment of these works, one of the major challenges is to avoid the interference of irrelevant behaviors in other irrelevant areas, so it is necessary to judge whether the target is in a specific sensing area or not, which means that the system should be able to determine exactly which side of the boundary line the target is on. However, the existing work cannot find a way to accurately monitor a freely set boundary, which hinders the actual implementation of WiFi-based sensing applications. In order to solve this problem, based on the physical essence of electromagnetic wave diffraction and the Fresnel diffraction model, this study finds a signal feature, namely Rayleigh distribution in Fresnel diffraction model (RFD), when the target passes through the link (the line between the WiFi receiver and transmitter antennas) and reveals the mathematical relationship between the signal feature and human activity. Then, the study realizes a boundary monitoring algorithm through line crossing detection by using the link as the boundary and considering the waveform delay caused by antenna spacing and the features of automatic gain control (AGC) when the link is blocked. On this basis, the study also implements two practical applications, that is, intrusion detection system and home state detection system. The intrusion detection system achieves a precision of more than 89% and a recall rate of more than 91%, while the home state detection system achieves an accuracy of more than 89%. While verifying the availability and robustness of the boundary monitoring algorithm, the study also shows the great potential of combining the proposed method with other WiFi-based sensing technologies and provides a direction for the actual deployment of WiFi-based sensing technologies.