Robust WiFi Respiration Sensing in the Presence of Interfering Individual

WiFi-based respiration sensing technology has gained increasing attention due to its contactless sensing capabilities and utilization of existing WiFi devices. However, existing studies are limited to certain scenarios without addressing the motion interference from other individuals. In this paper, we tackle the challenge of robust respiration sensing in the presence of other individuals. Specifically, through an in-depth examination of the correlation between respiratory signals and spatial beam patterns, we develop a respiratory-energy based approach to evaluate the diverse impact of dynamic interference on respiratory signals. When significant interference is detected, we employ a convex-optimization-based beam control strategy, which exploits the inherent characteristics of human respiration, to adaptively adjust the spatial beam pattern. This approach enables a robust and precise gain adjustment between the target and interfering individual, effectively mitigating the impact of interference. Experimental results demonstrate that our approach can reduce the mean absolute error (MAE) of respiration detection by up to 32% compared to state-of-the-art methods, significantly enhancing the accuracy and robustness of WiFi-based respiration sensing.