A Real-Time Respiration Monitoring System Using WiFi Sensing Based on the Concentric Circle Model

This paper proposes and experimentally validates a novel concentric circle (CC) model for indoor WiFi sensing. By setting the transmitter and receiver together, the perception model becomes concentric circles with equal spacing, eliminating the blind zone and unequal radial sensitivity problems of the Fresnel zone (FZ) model. Then a human respiratory monitoring system is developed based on this model, which executes the following steps: (1) Principal component analysis (PCA) is applied to the channel state information ratio (CSIR) as a preprocessing to extract the components related to human activities. (2) Human presence and respiratory signal detection are adopted to improve monitoring accuracy. (3) The Doppler respiratory frequency is extracted to calculate the respiratory rate. Experimental results show that the CC model achieves high accuracy in velocity measurement with an error of less than 0.4 cm/s. The respiration monitoring system can accurately monitor human respiration with an error of less than 0.7 bpm within 6 m.