  Blood oxygen saturation (SpO$_2$) is an essential indicator of respiratory
functionality and is receiving increasing attention during the COVID-19
pandemic. Clinical findings show that it is possible for COVID-19 patients to
have significantly low SpO$_2$ before any obvious symptoms. The prevalence of
cameras has motivated researchers to investigate methods for monitoring SpO$_2$
using videos. Most prior schemes involving smartphones are contact-based: They
require a fingertip to cover the phone's camera and the nearby light source to
capture re-emitted light from the illuminated tissue. In this paper, we propose
the first convolutional neural network based noncontact SpO$_2$ estimation
scheme using smartphone cameras. The scheme analyzes the videos of a
participant's hand for physiological sensing, which is convenient and
comfortable, and can protect their privacy and allow for keeping face masks on.
We design our neural network architectures inspired by the optophysiological
models for SpO$_2$ measurement and demonstrate the explainability by
visualizing the weights for channel combination. Our proposed models outperform
the state-of-the-art model that is designed for contact-based SpO$_2$
measurement, showing the potential of our proposed method to contribute to
public health. We also analyze the impact of skin type and the side of a hand
on SpO$_2$ estimation performance.
