Wi-locind: Location-Independent Respiration Sensing Based on WIFI CSI

Current WiFi-based respiration detection algorithms may experience performance degradation due to variations in user location, as the relationship between user location and patterns of respiration has not been adequately considered. To overcome this limitation, this paper proposes a spatially directional respiration detection approach, named Wi-locind. Wi-locind employs antenna arrays on commercial WiFi receivers to achieve directional enhancement of respiration signals. Combined with post-filtering techniques, Wi-locind is capable of extracting respiration patterns that are independent of changes in the user's location. Specifically, the Minimum Variance Distortionless Response algorithm is used to identify the arrival angle of the target user and directionally enhance the received signal in the corresponding direction. The Empirical Mode Decomposition algorithm is subsequently utilized to suppress the environmental noise and time domain artifacts caused by the enhancement method, enabling the extraction of the target's respiration pat-tern. Our results show that the proposed approach consistently achieves an average absolute error of less than 0.3 breaths per minute across all positions, significantly outperforming the baseline approaches.