State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.
National Research Center for Rehabilitation Technical Aids, Beijing 100176, China.
Sensors (Basel). 2023 Jul 26;23(15):6681. doi: 10.3390/s23156681.
Noncontact vital sign monitoring based on radar has attracted great interest in many fields. Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. This paper proposes a new algorithm for HRV monitoring in which frequency-modulated continuous-wave (FMCW) radar is used to separate echo signals from different distances, and the beamforming technique is adopted to improve signal quality. After the phase reflecting the chest wall motion is demodulated, the acceleration is calculated to enhance the heartbeat and suppress the impact of respiration. The time interval of each heartbeat is estimated based on the smoothed acceleration waveform. Finally, a joint optimization algorithm was developed and is used to precisely segment the acceleration signal for analyzing HRV. Experimental results from 10 participants show the potential of the proposed algorithm for obtaining a noncontact HRV estimation with high accuracy. The proposed algorithm can measure the interbeat interval (IBI) with a root mean square error (RMSE) of 14.9 ms and accurately estimate HRV parameters with an RMSE of 3.24 ms for MEAN (the average value of the IBI), 4.91 ms for the standard deviation of normal to normal (SDNN), and 9.10 ms for the root mean square of successive differences (RMSSD). These results demonstrate the effectiveness and feasibility of the proposed method in emotion recognition, sleep monitoring, and heart disease diagnosis.
基于雷达的非接触式生命体征监测在许多领域引起了极大的兴趣。心率变异性（HRV）用于衡量心跳间隔的波动，被认为是评估整体健康状况的重要指标。本文提出了一种新的 HRV 监测算法，该算法使用调频连续波（FMCW）雷达来分离来自不同距离的回波信号，并采用波束形成技术来提高信号质量。解调反映胸壁运动的相位后，计算加速度以增强心跳并抑制呼吸的影响。基于平滑后的加速度波形来估计每个心跳的时间间隔。最后，开发了一种联合优化算法，用于精确分割加速度信号以分析 HRV。来自 10 名参与者的实验结果表明，该算法具有获得高精度非接触式 HRV 估计的潜力。所提出的算法可以以 14.9ms 的均方根误差（RMSE）测量心动间隔（IBI），并以 RMSE 为 3.24ms 精确估计 HRV 参数，用于 MEAN（IBI 的平均值）、SDNN（正常到正常的标准差）为 4.91ms 和 RMSSD（连续差异的均方根）为 9.10ms。这些结果表明，该方法在情感识别、睡眠监测和心脏病诊断中的有效性和可行性。
