A Real-Time Vital Sign Monitoring and Human Identification Using WiFi Signals

WiFi sensing represents an emerging technology that enables human sensing in a low-cost, non-invasive manner. Respiration, as a vital sign, can be employed not only for the assessment of physiological conditions but also for human identification. This paper proposes a highly accurate real-time vital sign monitoring algorithm and a high-precision person identification algorithm. The real-time monitoring algorithm utilizes WiFi device antenna diversity and Maximum Ratio Combining Discrete Wavelet Transform (MRC-DWT) to recover respiration signals, combining with the Autocorrelation Function (ACF) for accurate instantaneous respiration estimation. The identification algorithm leverages biometric information from respiration waveforms and the Multi-dimensional Dynamic Time Warping (MD-DTW) algorithm with modern classifiers for high-precision recognition. This paper validates the efficacy of the proposed algorithms by leveraging the WiFi Respiration Track (WiRes-Track) System. Experimental results demonstrate that the system is capable of discriminating among multiple users, attaining an average recognition accuracy of 91% for six users and a respiration rate accuracy of 96.8%