Range Expansion Method for WiFi-Based Respiration Monitoring Under Dynamic Scenes

WiFi-based respiratory monitoring becomes a hot spot in the sensing layer of IoT benefiting from non contact, low cost and high privacy protection. However, current WiFi-Based respiratory monitoring methods relay on sensitive channel state information (CSI) samples which requires that single monitoring target keeps static without any moving non-target person and closing to the WiFi transceiver device. These requirements limit the large-scale applications of WiFi-based respiratory monitoring. Therefore, we propose a respiratory monitoring range extension method named FDRadio, which is able to work under dynamic interference scenes. In FDRadio, we improve the accuracy and robustness of respiratory monitoring from three aspects: separating dynamic interference sources, eliminating ambient noise and enhancing power of dynamic reflected signal. Specifically, we first expand the channel bandwidth by combining multiple WiFi channels to improve the spatial resolution of WiFi sensing, and employ wired direct channel to remove the accumulated hardware noise caused by combining channels. Second, we analyze the relationship between monitoring range and ambient noise, and then adopt time diversity techniques to design a two-stage ambient noise deduction process for FDRadio. In addition, we design a novel weight allocation algorithm, which maximizes the dynamic reflected signal power, and enhances the ability to sensing weak chest fluctuation caused by breath. Finally, the processed CSI samples are converted to power delay spectrum (PDP) in time domain. By this, the respiratory signal can be directly extracted from the target person using the distance difference. We have implemented FDRadio on a commercial embedded devices and conduct a series of experiments. The experimental results show that detection error is less than 0.5 bpm under the 7m available monitoring range, even if multiple moving non-target person exist.