An Optimization Method of Human Vital Signs Detection During the Non-Steady States

Because the millimeter-wave radar based on frequency-modulated continuous wave (FMCW) owns the advantages of non-contact, privacy protection, high resolution, and anti-interference, applying it in monitoring vital signs gradually becomes a trend. However, most researchers focus on how to improve the detection performance under the steady-state state and give little consideration to the detection under the non-steady state with physical disturbance. We propose a method to detect vital signs in a best-effort way. This method recognizes the steady state and non-steady state, and the type of motion obtains the vital signs without physical disturbance. For this end, we first compute feature spectrograms with range-main velocity information from motion features. And then, employ slide windows sampling to construct datasets, and train the ResNet-18 network to identify the motion state and the motion type. Finally, based on the result of the motion state, we extract the phase signal during exercise rest and use the variational mode decomposition (VMD) algorithm to analyze the respiration rate and heart rate. Experiment results show that using the trained network model, the recognition accuracy of the motion state and motion type is close to 97%, and the recognition delay is less than 1.1 s. Meanwhile, after motion recognition, the mean absolute deviation (MAD) of the detection respiration and heart rate drops to 1.7 bpm and 3.4 bpm respectively.