  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.
