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