Hao Zhanjun, Wang Yue, Li Fenfang, Ding Guozhen, Fan Kai, Gao Yifei
College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China.
Gansu Province Internet of Things Engineering Research Center, Northwest Normal University, Lanzhou, 730070, China.
Sci Rep. 2025 Aug 1;15(1):28112. doi: 10.1038/s41598-025-09112-w.
With the growing demand for health monitoring, non-contact vital signs monitoring technology has garnered widespread attention. While traditional health monitoring methods are accurate, they have limitations in terms of non-contact and non-invasive capabilities. This paper proposes a non-contact vital signs monitoring method based on frequency modulated continuous wave (FMCW) millimeter-wave radar, named MRVS, to enhance both convenience and accuracy. The method consists of three steps: signal processing, decomposition, and reconstruction. Firstly, the millimeter-wave radar is used to detect chest movements, extracting both respiration and heartbeat signals. Then, combining signal superposition with phase difference techniques effectively eliminates static clutter and respiratory harmonic interference, enhancing the signal. Next, discrete wavelet transform (DWT) is utilized to suppress clutter and noise further, performing signal decomposition. The reconstruction module employs an adaptive Kalman filter (AKF) combined with square root normalization for accurate heart rate estimation. Experimental results demonstrate that this method achieves an estimation error rate of less than 7% under different distances, angles, and postures, showcasing high accuracy and robustness and providing a new solution for non-contact vital signs monitoring.
随着对健康监测需求的不断增长，非接触式生命体征监测技术受到了广泛关注。虽然传统的健康监测方法准确，但在非接触和非侵入能力方面存在局限性。本文提出了一种基于调频连续波（FMCW）毫米波雷达的非接触式生命体征监测方法，名为MRVS，以提高便利性和准确性。该方法包括三个步骤：信号处理、分解和重建。首先，毫米波雷达用于检测胸部运动，提取呼吸和心跳信号。然后，将信号叠加与相位差技术相结合，有效消除静态杂波和呼吸谐波干扰，增强信号。接下来，利用离散小波变换（DWT）进一步抑制杂波和噪声，进行信号分解。重建模块采用自适应卡尔曼滤波器（AKF）结合平方根归一化进行准确的心率估计。实验结果表明，该方法在不同距离、角度和姿势下的估计误差率均小于7%，具有较高的准确性和鲁棒性，为非接触式生命体征监测提供了一种新的解决方案。
