Remote Measurement of Human Vital Signs Based on Joint-Range Adaptive EEMD

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. INDEX TERMS Non-contact vital sign detection, ensemble empirical mode decomposition (EEMD), frequency-modulated continuous-wave (FMCW), multiple measurement vectors (MMV), mm-wave radar.

RF-Heartbeat: Robust and Contactless Heartbeat Monitoring Based on FMCW Radar

“…For each time window, the average heart rate is estimated by spectrum analysis [23]. Some innovative algorithms use wavelet transform [24], non-negative matrix factorization [17], embedded ensemble empirical mode decomposition (EEMD) [27], etc. to recover the heartbeat harmonics under the interference of respiration and body movements, intend to obtain a more accurate estimate.…”

Joint Waveform/Receiver Design for Vital-Sign Detection in Signal-Dependent Interference

“…is the target spatial Doppler vector, containing the desired breathing or heartbeat signal. According to previous work [24]- [26], the displacement signal is usually assumed to be sinusoidal with constant amplitude and frequency for the duration of one CPI. Standard amplitude values are in the range of 4 mm to 12 mm for breathing [27] and 0.2 mm to 0.5 mm for heartbeat [28].…”

Non-Contact Heart Rate Monitoring Based on Millimeter Wave Radar

“…The radar used in non-contact vital sign monitoring is mainly composed of continuous-wave (CW) radar, impulse radio ultra-wideband (IR-UWB) radar, and frequency-modulated continuous-wave (FMCW) radar [13], [14]. FMCW Millimeter Wave (mmWave) radar has an excellent performance in distance measurement and micro-movement measurement [15], [16]. In addition, velocity measurement and angle measurement can be performed through the MIMO antenna arrays [17], [18].…”