论文标题
在临床环境中雷达的高保真生命体征估计的欧拉阶段运动放大倍率
Eulerian Phase-based Motion Magnification for High-Fidelity Vital Sign Estimation with Radar in Clinical Settings
论文作者
论文摘要
根据需要进行生命符号监测，在嘈杂环境中从小物体产生的微妙运动的有效，准确检测是具有挑战性的，但可以通过放大倍数大大改善。我们开发了一种复杂的基于Gabor滤波器的分解方法，以放大不同空间波长水平的相位，以放大运动并提取1D运动信号以进行基本频率估计。处理基于阶段的复杂Gabor滤波器输出，然后用于训练机器学习模型，以更准确地预测呼吸和心率。我们表明，在临床环境（例如睡眠实验室和急诊科）以及各种人类姿势的临床环境中，我们提出的技术的性能要比常规的基于FFT的方法更好。
Efficient and accurate detection of subtle motion generated from small objects in noisy environments, as needed for vital sign monitoring, is challenging, but can be substantially improved with magnification. We developed a complex Gabor filter-based decomposition method to amplify phases at different spatial wavelength levels to magnify motion and extract 1D motion signals for fundamental frequency estimation. The phase-based complex Gabor filter outputs are processed and then used to train machine learning models that predict respiration and heart rate with greater accuracy. We show that our proposed technique performs better than the conventional temporal FFT-based method in clinical settings, such as sleep laboratories and emergency departments, as well for a variety of human postures.
