  The estimation and monitoring of SpO2 are crucial for assessing lung function
and treating chronic pulmonary diseases. The COVID-19 pandemic has highlighted
the importance of early detection of changes in SpO2, particularly in
asymptomatic patients with clinical deterioration. However, conventional SpO2
measurement methods rely on contact-based sensing, presenting the risk of
cross-contamination and complications in patients with impaired limb perfusion.
Additionally, pulse oximeters may not be available in marginalized communities
and undeveloped countries. To address these limitations and provide a more
comfortable and unobtrusive way to monitor SpO2, recent studies have
investigated SpO2 measurement using videos. However, measuring SpO2 using
cameras in a contactless way, particularly from smartphones, is challenging due
to weaker physiological signals and lower optical selectivity of smartphone
camera sensors. The system includes three main steps: 1) extraction of the
region of interest (ROI), which includes the palm and back of the hand, from
the smartphone-captured videos; 2) spatial averaging of the ROI to produce R,
G, and B time series; and 3) feeding the time series into an
optophysiology-inspired CNN for SpO2 estimation. Our proposed method can
provide a more efficient and accurate way to monitor SpO2 using videos captured
from consumer-grade smartphones, which can be especially useful in telehealth
and health screening settings.
