Contactless Blood Oxygen Saturation Estimation from Facial Videos Using Deep Learning

• Published in: Bioengineering (Basel) Vol. 11; no. 3; p. 251
• Main Authors: Cheng, Chun-Hong, Yuen, Zhikun, Chen, Shutao, Wong, Kwan-Long, Chin, Jing-Wei, Chan, Tsz-Tai, So, Richard H. Y.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.03.2024; MDPI
• Subjects: Artificial neural networks; Blood; blood oxygen saturation measurement; Body temperature; Cameras; Computer vision; COVID-19; Datasets; Deep learning; Face; facial videos; Health aspects; Hemoglobin; Infectious diseases; Light; Machine learning; Measuring instruments; Neural networks; non-contact monitoring; Oximetry; Oxygen; Oxygen content; Oxygen saturation; Pandemics; Physiology; Pulse oximetry; Ratios; remote health monitoring; Remote monitoring; Representations; Sensitivity analysis; Signal processing; Smartphones; Video; Vital signs; United Kingdom; deep learning; remote health monitoring; blood oxygen saturation measurement; facial videos; non-contact monitoring
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering11030251

Blood oxygen saturation (SpO2) is an essential physiological parameter for evaluating a person’s health. While conventional SpO2 measurement devices like pulse oximeters require skin contact, advanced computer vision technology can enable remote SpO2 monitoring through a regular camera without skin contact. In this paper, we propose novel deep learning models to measure SpO2 remotely from facial videos and evaluate them using a public benchmark database, VIPL-HR. We utilize a spatial–temporal representation to encode SpO2 information recorded by conventional RGB cameras and directly pass it into selected convolutional neural networks to predict SpO2. The best deep learning model achieves 1.274% in mean absolute error and 1.71% in root mean squared error, which exceed the international standard of 4% for an approved pulse oximeter. Our results significantly outperform the conventional analytical Ratio of Ratios model for contactless SpO2 measurement. Results of sensitivity analyses of the influence of spatial–temporal representation color spaces, subject scenarios, acquisition devices, and SpO2 ranges on the model performance are reported with explainability analyses to provide more insights for this emerging research field.