LAUSR

Respiration monitoring (RM) is crucial for tracking various health problems. Recently, RFID has been widely employed for lightweight and low-cost RM. However, existing RFID-based RM systems are designed for static environments where no people move around the monitored person. While, in practice, most environments are dynamic with people moving nearby, which introduces dynamic multipath signals and significantly distorts the respiration signal, leading to inaccurate RM. In this paper, we aim to realize accurate RFID-based RM in dynamic environments. Our observations show that multipath signals can result in a similar pattern to respiration, which leads to mis-detection of apnea and inaccurate respiration rate estimation. To address this issue, we first measure the respiration anomaly in the signal spectrogram to detect apnea. Second, we successfully remove the multipath effect for respiration rate estimation inspired by the intrinsic features of human respiration. Specifically, compared with people’s moving pattern, respiration pattern is regular and periodic. By transforming a normal respiration cycle into a matched filter, real respiration cycles can be extracted from the noisy RFID signal, which can be applied to estimate the respiration rate via peak detection scheme. The experiments show that our system achieves the average error of 4.2% and $0.51\ bpm$0.51bpm for apnea detection and respiration rate estimation in dynamic environments, respectively.