Resilient Respiration Rate Monitoring With Realtime Bimodal CSI Data

Vital signs, such as respiration rate, can provide useful information for personal healthcare. In this paper, we present ResBeat, a commodity 5 GHz WiFi based system to exploit bimodal channel state information (CSI), including amplitude and phase difference, for realtime, long-term, and contact-free respiration rate monitoring. Specifically, we first present an analysis of breathing signal anomalies based on bimodal CSI data. Then, we present the design of the data preprocessing, adaptive signal selection, and breathing signal monitoring modules of ResBeat, and employ peak detection to estimate respiration rates. We conduct extensive experiments on respiration rate monitoring under three different environments, where superior performance over two alternative methods is demonstrated.