Eulerian Phase-based Motion Magnification for High-Fidelity Vital Sign Estimation with Radar in Clinical Settings
This work addresses the problem of accurate vital sign estimation for patients in clinical settings, representing an incremental improvement over existing methods.
The paper tackled the challenge of detecting subtle motion for vital sign monitoring in noisy clinical environments by developing a complex Gabor filter-based decomposition method to magnify motion and extract signals for frequency estimation, resulting in improved accuracy for predicting respiration and heart rates compared to conventional FFT-based methods.
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.