Respiratory Monitoring Using Millimeter-Wave Base Stations Based on OFDM Signals

With the development of the Internet of Things (IoT) and communication technologies, there is a growing demand for intelligent living. Respiration is one of the vital physiological activities in the human body, and it has wide applications in intelligent healthcare, home monitoring, and sleep detection. Traditional contact-based sensor monitoring methods are difficult to promote in home settings. This paper utilizes millimeter-wave (mm Wave) communication base stations for non-contact respiratory detection, effectively addressing the issues of numerous interference sources and low accuracy in the sub-6 GHz frequency band. Firstly, a mathematical model is derived to describe the systematic phase fluctuations of wireless signals caused by respiratory. Subsequently, the CSI phase is unwrapped and filtered. Finally, the processed phase undergoes Empirical Wavelet Transform (EWT) signal decomposition to extract the respiratory component. Fast Fourier Transform (FFT) is then applied to obtain the respiratory spectrum, and the respiratory rate is estimated by searching for the peak in the spectrum. The experimental results validate the feasibility and accuracy of the proposed system and algorithm, achieving a recognition rate 94% in measuring respiratory rates.