RaliSense: Extending WiFi Respiratory Detection Range by Rapid Alignment of Dynamic Components

Abstract

WiFi based respiratory detection has attracted increasing attentions due to its ubiquity and convenience. In Non-Line-of-Sight (NLoS) scenarios, WiFi signals reflected from human target are blocked by obstacles and become much weaker, thus limiting the sensing range and hindering the practical deployment. The existing best respiratory detection system extended the sensing range by scaling and aligning dynamic components in WiFi signals. However, its dynamic component scaling causes the amplification of noise, while its dynamic component alignment increases computation complexity due to the traversal on all possible rotation angles. To address the above issues, in this paper we first build WiFi sensing range models for respiratory detection in NLoS scenario, find factors that limit the sensing range, and then propose a new respiratory detection system named RaliSense which can further rapidly extend the sensing range in NLoS scenario. The main idea of RaliSense is rapidly aligning dynamic components without amplifying noise, based on change direction vector and CSI ratio sum polarity of dynamic components. The proposed change direction vector is obtained by calculating the direction on which the noisy dynamic components have the maximum variance, and CSI ratio sum polarity is then obtained by summing the dynamic components which have been rotated by the change direction vector. According to the CSI ratio sum polarity, the rotation angle is quickly adjusted for aligning dynamic components. Extensive simulation and experiment results verify the effectiveness of our proposed sensing range models. The results also demonstrate that our proposed system RaliSense can effectively extend sensing range in NLoS scenario, achieving a 22.7% improvement over the best existing work but spending only a quarter of its computation time.