Millimeter-Wave Radar-Based Unsteady Vital Signs Monitoring for Smart Home

The millimeter-wave(mm-Wave) radar based on frequency-modulated continuous wave (FMCW) owns the advantages of non-contact, privacy protection, high resolution, and anti-interference, and it became the hot point that applying the radar in monitoring vital signs for smart home. However, most of current studies focus on how to improve the detection performance under the steady scenarios and give little consideration on the unsteady scenarios with physical interference. In this paper, we propose a method to detect vital signs under unsteady scenarios by a best-effort way. This method automatically differentiates between the steady state and motion state (unsteady) by identifying the motion type, and extracts the vital sign under steady state without physical motion interference. For this end, we first figure out feature spectrograms with range-main velocity information from motion features. And then, employ a sliding windows sampling method to construct data set, and apply ResNet-18 network model in the motion type identification (including steady state). Based on the motion type, the phase signal during steady state and leverage the variational mode decomposition (VMD) algorithm to analyze respiration/heart rate. Experiment results show that using ResNet-18, the recognition accuracy of the motion state and motion type is close to 97%, and the recognition delay is less than 1.1s. Meanwhile, the mean absolute errors of the respiration rate and heart rate drop to 1.7bpm and 3.4bpm respectively.