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Remote Monitoring of Human Vital Signs Based on 77-GHz mm-Wave FMCW Radar

机译:基于77 GHz毫米波FMCW雷达的人类生命体征远程监控

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摘要

In recent years, non-contact radar detection technology has been able to achieve long-term and long-range detection for the breathing and heartbeat signals. Compared with contact-based detection methods, it brings a more comfortable and a faster experience to the human body, and it has gradually received attention in the field of radar sensing. Therefore, this paper extends the application of millimeter-wave radar to the field of health care. The millimeter-wave radar first transmits the frequency-modulated continuous wave (FMCW) and collects the echo signals of the human body. Then, the phase information of the intermediate frequency (IF) signals including the breathing and heartbeat signals are extracted, and the Direct Current (DC) offset of the phase information is corrected using the circle center dynamic tracking algorithm. The extended differential and cross-multiply (DACM) is further applied for phase unwrapping. We propose two algorithms, namely the compressive sensing based on orthogonal matching pursuit (CS-OMP) algorithm and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) algorithm, to separate and reconstruct the breathing and heartbeat signals. Then, a frequency-domain fast Fourier transform and a time-domain autocorrelation estimation algorithm are proposed to calculate the respiratory and heartbeat rates. The proposed algorithms are compared with the contact-based detection ones. The results demonstrate that the proposed algorithms effectively suppress the noise and harmonic interference, and the accuracies of the proposed algorithms for both respiratory rate and heartbeat rate reach about 93%.
机译:近年来,非接触式雷达检测技术已经能够实现对呼吸和心跳信号的长期和远距离检测。与基于接触的检测方法相比,它给人体带来了更舒适,更快的体验,并逐渐受到雷达传感领域的关注。因此,本文将毫米波雷达的应用扩展到医疗保健领域。毫米波雷达首先发送调频连续波(FMCW)并收集人体的回声信号。然后,提取包括呼吸和心跳信号的中频(IF)信号的相位信息,并使用圆心动态跟踪算法校正相位信息的直流(DC)偏移。扩展的差分和交叉乘法(DACM)进一步用于相位展开。我们提出两种算法,分别是基于正交匹配追踪的压缩感知(CS-OMP)算法和基于离散小波变换的严格自适应软阈值降噪(RA-DWT)算法,以分离和重构呼吸信号和心跳信号。然后,提出了一种频域快速傅里叶变换和时域自相关估计算法来计算呼吸频率和心跳频率。将该算法与基于接触的检测算法进行了比较。结果表明,该算法有效抑制了噪声和谐波干扰,其呼吸频率和心跳频率的准确性均达到93%左右。

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