  Blood oxygen saturation (SpO$_2$) is an important indicator for pulmonary and
respiratory functionalities. Clinical findings on COVID-19 show that many
patients had dangerously low blood oxygen levels not long before conditions
worsened. It is therefore recommended, especially for the vulnerable
population, to regularly monitor the blood oxygen level for precaution. Recent
works have investigated how ubiquitous smartphone cameras can be used to infer
SpO$_2$. Most of these works are contact-based, requiring users to cover a
phone's camera and its nearby light source with a finger to capture reemitted
light from the illuminated tissue. Contact-based methods may lead to skin
irritation and sanitary concerns, especially during a pandemic. In this paper,
we propose a noncontact method for SpO$_2$ monitoring using hand videos
acquired by smartphones. Considering the optical broadband nature of the red
(R), green (G), and blue (B) color channels of the smartphone cameras, we
exploit all three channels of RGB sensing to distill the SpO$_2$ information
beyond the traditional ratio-of-ratios (RoR) method that uses only two
wavelengths. To further facilitate an accurate SpO$_2$ prediction, we design
adaptive narrow bandpass filters based on accurately estimated heart rate to
obtain the most cardiac-related AC component for each color channel.
Experimental results show that our proposed blood oxygen estimation method can
reach a mean absolute error of 1.26% when a pulse oximeter is used as a
reference, outperforming the traditional RoR method by 25%.
