Abstract
Remote techniques for measuring human vital signs have attracted great interests due to the benefits shown in medical monitoring and military applications. Compared with continuous-wave Doppler radar, frequency-modulated continuous-wave (FMCW) radar which can discriminate vital signs from different distances, shows potential for reducing the interferences from other targets and the environment. However, in the state-of-the-art algorithms, only one chirp per frame is utilized for FMCW-based vital sign monitoring. Moreover, the vital signal is extracted from only one range bin of the fast Fourier transform. Which does not make full utilization of the long system idle time, and loses the power distributed on other range bins. By exploiting the relationship between respiration and heartbeat vibrations, an adaptive identification embedded ensemble empirical mode decomposition (EEMD) method for joint-range spectral estimation is proposed to measure the heart rate. First, A multi-chirp processing is presented for a 2-dimensional phase accumulation. Then the phase signals from a sequence of range bins are decomposed with a fast adaptive identification process. Finally, with the identified heartbeat components, we solve a multiple measurement vectors problem to estimate the heart rate. Experimental results showed that, at the detection range from 1 m~ 2.5 m, the proposed method can robustly distinguish the heartbeat from respiration and its harmonics and accurately estimate the heart rate with a root mean square error less than 6 bpm.
Highlights
Remote monitoring of human vital signs has attracted great interests in various fields, such as medical monitoring, military applications, security and counter-terrorism action, as well as search and rescue operations [1]–[8]
PROPOSED METHOD The conventional signal processing of vital sign measurement based on frequency-modulated continuous-wave (FMCW) radar can be divided into three stages, i.e., the range fast Fourier transform (FFT), the vital signal extraction, and the frequency estimation
Compared with empirical mode decomposition (EMD), the respiration and its low-order harmonic components have been filtered out in the spectrograms of the heartbeat intrinsic mode functions (IMFs) given by ensemble empirical mode decomposition (EEMD), which indicates that the problem of modemixing has been alleviated to a certain extent
Summary
Remote monitoring of human vital signs has attracted great interests in various fields, such as medical monitoring, military applications, security and counter-terrorism action, as well as search and rescue operations [1]–[8]. PROPOSED METHOD The conventional signal processing of vital sign measurement based on FMCW radar can be divided into three stages, i.e., the range FFT, the vital signal extraction, and the frequency estimation.
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