2022/12/03 by Md Farhan Tasnim Oshim, Oshim, Md Farhan Tasnim, Toral Surti +9 · 1 citation
Engineering · Medicine · #FOS: Computer and information sciences #FOS: Electrical engineering #Healthcare Technology and Patient Monitoring #Hemodynamic Monitoring and Therapy #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Non-Invasive Vital Sign Monitoring #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2212.04923
openalex publication_date 2022/12/03 · openalex created_date 2022/12/25 · openalex updated_date 2026/07/28
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.