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Motion-based Respiratory Rate Estimation with Motion Artifact Removal Technique in a Facial Video with an RGB Camera


Respiratory rate (RR) is a significant indicator
of health conditions. Remote contactless measurement of RR
is gaining popularity with recent respiratory tract infection
awareness. Among various methods of contactless RR measurement,
a frontal face video with an RGB camera can be used
to obtain an instantaneous RR. In this paper, we introduce an
RR estimation based on the subtle motion of head or upper
chest captured on an RGB camera. Motion-based respiratory
monitoring allows us to acquire RR from individuals even with
partial face covering, such as glasses or a face mask. However,
motion-based RR estimation is vulnerable to the subject’s
voluntary movement. In this work, adaptive selection between
face and chest regions plus a motion artifact removal technique
enable us to obtain a clean respiratory signal from facial
video recordings. The average mean absolute error (MAE)
for both controlled and natural breathing is 1.95 BPM using
head motion only and 1.28 BPM using chest motion only. Our
results demonstrate the possibility of continuous monitoring of
breathing rate in real-time with any personal device equipped
with camera, such as a laptop or smartphone.

Author: Migyeong Gwak, Korosh Vatanparvar, Jilong Kuang, Alex Gao

Published: Engineering in Medicine and Biology Conference (EMBC)

Date: Jul 11, 2022