Anti-interference non-contact breathing frequency detection method based on wifi

With the rapid development of wireless sensing technologies, WiFi channel state information (CSI) has become a powerful tool for monitoring human vital signs, such as breathing and body movement. However, estimating respiration rate at rest is often constrained by body motion and non-target signal interference. In this study, we address this challenge by proposing an innovative joint oriented vector technique that accurately distinguishes between target and non-target signals and effectively eliminates the interference of non-target signals. An adaptive sliding window is used to compute the differential standard deviation of the CSI amplitude information, which is combined with a signal-to-noise ratio-optimised subcarrier selection strategy and a PW-Hampel filter to significantly suppress the interference caused by body motion and hardware noise to the respiratory monitoring signals, and an improved peak detection algorithm is used to accurately identify peaks in the respiratory signals. The experimental results show that the proposed method achieves a high accuracy of 98.90% under body movement conditions, which significantly exceeds the level of existing technology.