Eulerian Phase-based Motion Magnification for High-Fidelity Vital Sign Estimation with Radar in Clinical Settings

TL;DR

This paper introduces a phase-based motion magnification technique using complex Gabor filters to enhance subtle vital sign signals from radar data, improving accuracy over traditional methods in noisy clinical environments.

Contribution

The authors propose a novel phase-based motion magnification method with Gabor filters and machine learning for improved vital sign estimation from radar signals in clinical settings.

Findings

Outperforms conventional FFT-based methods in clinical environments

Accurately estimates respiration and heart rate from radar signals

Effective across various human postures

Abstract

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.