Accurate Vital Signs Detection Based on FMCW Radar in Dynamic Environments

Non-contact vital signs detection plays an important role in smart healthcare. However, most of the existing algorithms focus on stationary environments and the proposed algorithms are computationally intensive. To address the existing problems, this paper proposes the envelope fitting-adaptive fast ensemble empirical mode decomposition (EF-AFEEMD) algorithm. The time-varying DC offset is estimated by fitting the envelope of the signal. Afterwards, the demodulated signal is decomposed into multiple IMFs by adaptively determining multiple parameters of the decomposition. Finally, the appropriate IMFs corresponding to the vital signs signals are selected by calculating the correlation coefficient and local energy ratio (LER) of each IMF. Simulation has validated the effectiveness of the proposed algorithm. Moreover, experimental results have shown that compared to EEMD and FEEMD, the proposed algorithm can ensure accuracy while greatly reducing computation, which is less than half of the computation time of other algorithms.