Remote photoplethysmography (rPPG) is an active area of research that has extended beyond heart rate (HR) estimation to estimate more complex cardiac signals such as HR variability and blood oxygen saturation (SpO 2). As these applications extend to more challenging domains, the SNR of the physiological signal is increasingly important. This research is particularly useful for enhancing the accessibility and impact of rPPG technologies in consumer electronic devices, such as RGB cameras. Almost all videos recorded with consumer devices use prevailing codec settings, and such video compression is a source of signal degradation. While prior works have assessed the performance of rPPG algorithms under various video compression rates, they have not explored how specific compression configurations can be strategically optimized to better preserve the integrity of rPPG signals. This paper aims to address this gap by identifying a close connection between the projection plane from the principled plane-orthogonal-to-skin (POS) algorithm to the YCbCr color space commonly used in compression. Namely, these projection planes are equivalent, which is a crucial connection that motivates the consideration of alternative color spaces for video compression in physiological monitoring applications. Improved rPPG signal preservation at low bitrates can be achieved using the RGB24 color space for compression compared to YUV444 and YUV420. These results suggest opportunities to reduce error in rPPG applications by treating the video compression color space as a tunable setting in a system's design.

Blood oxygen saturation (SpO2) 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 SpO2. 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 SpO2 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 SpO2 information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO2 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%.

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.