Frequency Modulated Continuous Wave (FMCW)-based mmWave radar has attracted widespread attention because of its non-contact and high spatial resolution for vital signs monitoring. Meanwhile, current studies focus mainly on how to improve the detection performance of steady multiple objects or unsteady single objects. In this work, we propose an innovative method for identity-based multi-object vital signs monitoring under unsteady scenarios. The method automatically distinguishes between steady and motion states, and conducts a best-effort vital signs monitoring during unsteady scenarios. To this end, we design a weight vector enhancement method combined with object spatial positioning for differentiating multiple objects, and identify each object according to the gait-based EfficientNet model. We also design a steady-state detector based on the MobileNet-V2 network to find the slots of object keeping steady for vital signs monitoring and then apply the variational mode decomposition (VMD) algorithm to extract the respiratory and heart rates of a single object. The experimental results showed that the mean absolute error of respiratory rate and heart rate decreased to 1.37 bpm and 2.56 bpm respectively in the case of multiple objects. In addition, the steady-state detector achieves close to 98.1% accuracy in recognizing motion types, and the average recognition rate of identity recognition based on gait features reaches about 93%.
Qiu et al. (Mon,) studied this question.