Peripheral Oxygen Saturation Measurement Using an RGB Camera
Bing-Jhang Wu, Bing‐Fei Wu, You-Cheng Dong, Hsiang‐Chun Lin, P. W. Li
Abstract
In 2019, the COVID-19 pandemic became a serious issue around the world. Low blood oxygen is the most important symptom of COVID-19. However, many patients have no obvious respiratory symptoms and exhibit silent hypoxemia, which is typically not noticed by patients but can cause severe damage. Hypoxemia is also related to high-altitude illness, highlighting the importance of detecting hypoxemia for travelers in high-altitude areas. The most commonly used device for monitoring blood oxygen remains difficult to access for the general population. To address this problem, a few camera-based methods have been proposed. Nevertheless, these approaches are generally not robust and have not been evaluated completely. Therefore, we conduct a pilot study to show the feasibility of camera-based SpO2 estimation and then propose a contact-free system by which to measure SpO2 using a general red–green–blue (RGB) camera. We adopt a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbor (KNN) model as the backbone algorithm. For a comprehensive evaluation, we compiled two SpO2 datasets: one collected at an altitude of 3150 m, and the other collected at 102 m above sea level. Sixty subjects participated in the experiments, which included a mobile phone, a webcam, and an industrial camera. In a leave-one-subject-out (LOSO) validation, the proposed method, respectively, yields a mean absolute error (MAE) of 4.39%, 4.45%, and 4.22% using the three cameras. In general, the proposed approach outperforms the benchmark algorithms. To the best of our knowledge, this is the first work to utilize real high-attitude data to address camera-based SpO2 measurement.