Abstract
Blood oxygen saturation (SpO2) 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 SpO2
before any obvious symptoms. The prevalence of cameras has motivated
researchers to investigate methods for monitoring SpO2
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 SpO2 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 SpO2 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 SpO2 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 SpO2 estimation performance.