Exploring stability in wifi sensing system based on fresnel zone model

WiFi based contactless sensing systems use pervasive wireless communication signals in the environment to sense human activities in a natural way, enabling many promising applications. From fine-grained activity sensing to coarse-grained activity recognition, existing work have done a great deal of exploration. However, there is lack of understanding and tackling the serious unstable sensing performance problem. While changing the human target, the position of transceivers, and test environment, the system performance is severely degraded. The reason behind the instability of WiFi-based sensing system is that human activities induce the inconsistent signal patterns inherently at different positions. This paper proposes the Fresnel zone-based diffraction and reflection sensing model, which can be used to accurately quantify the relationship between the target’s position with respect to the transceiver, movement trajectory and the signal variation pattern. By illustrating two application examples, i.e., fine-grained finger gesture recognition and coarse-grained fitness activity recognition, and guided by the sensing model, this paper explores the reason behind the unstable performance for sensing system. This paper clearly explains how to obtain the consistent signal patterns and how to generate easily distinguishable signal patterns, further presents the methods to improve the performance of wireless sensing systems.