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.

翻译：我们开发了一个复杂的加博过滤分解法,以扩大不同空间波长水平的阶段,放大运动并提取1D运动信号,以便进行基本频率估计。基于阶段的复合加博过滤器产出经过处理,然后用于培训机器学习模型,以便更准确地预测呼吸和心率。我们显示,我们提出的技术在临床环境,例如睡眠实验室和应急部门,以及各种人类姿势,比传统的短期FFFT法效果更好。