TY - GEN
T1 - Eulerian Phase-based Motion Magnification for High-Fidelity Vital Sign Estimation with Radar in Clinical Settings
AU - Tasnim Oshim, Md Farhan
AU - Surti, Toral
AU - Goldfine, Charlotte
AU - Carreiro, Stephanie
AU - Ganesan, Deepak
AU - Jayasuriya, Suren
AU - Rahman, Tauhidur
N1 - Funding Information:
ACKNOWLEDGMENT This work is in part supported by the National Science Foundation under grant SBE 1839999, grant SCH 2124282, TSA grant UL1 TR001863 from the National Center for Advancing Translational Science (NCATS, components of the National Institute of Health), NARSAD Brain and Behavior Research Foundation (TS), and start-up grant support from the Manning College of Information and Computer Sciences and the Institute for Applied Life Sciences at the University of Massachusetts Amherst.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Clinical Settings
KW - Gabor Filter
KW - Motion Magnification
KW - UWB Radar
KW - Vital Sign Estimation
UR - http://www.scopus.com/inward/record.url?scp=85144017137&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85144017137&partnerID=8YFLogxK
U2 - 10.1109/SENSORS52175.2022.9967051
DO - 10.1109/SENSORS52175.2022.9967051
M3 - Conference contribution
AN - SCOPUS:85144017137
T3 - Proceedings of IEEE Sensors
BT - 2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Sensors Conference, SENSORS 2022
Y2 - 30 October 2022 through 2 November 2022
ER -