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Recent research has highlighted the detection of human respiration rate usingcommodity WiFi devices. Nevertheless, these devices encounter challenges inaccurately discerning human respiration amidst the prevailing human motioninterference encountered in daily life. To tackle this predicament, this paperintroduces a passive sensing and communication system designed specifically forrespiration detection in the presence of robust human motion interference.Operating within the 60.48 GHz band, the proposed system aims to detect humanrespiration even when confronted with substantial human motion interferencewithin close proximity. Subsequently, a neural network is trained using thecollected data by us to enable human respiration detection. The experimentalresults demonstrate a consistently high accuracy rate over 90\% of the humanrespiration detection under interference, given an adequate sensing duration.Finally, an empirical model is derived analytically to achieve the respiratoryrate counting in 10 seconds.

Passive Respiration Detection via mmWave Communication Signal Under Interference