# Remote Blood Oxygen Estimation From Videos Using Neural Networks

arXiv: https://arxiv.org/abs/2107.05087 · HTML: https://mlsanity.com/paper/2107.05087/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 56 references that are in the map.
- First posted on arXiv 11 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks](https://mlsanity.com/paper/1805.07888/) (2018)
- [RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation](https://mlsanity.com/paper/1910.11515/) (2019)

## Built on by (most cited first, 5 of 5)

- [JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA](https://mlsanity.com/paper/2404.11525/) (2024)
- [Blood Oxygen Saturation Estimation from Facial Video via DC and AC components of Spatio-temporal Map](https://mlsanity.com/paper/2212.07116/) (2022)
- [Contactless Oxygen Monitoring with Gated Transformer](https://mlsanity.com/paper/2212.03357/) (2022)
- [An AI-Enabled Framework Within Reach for Enhancing Healthcare Sustainability and Fairness](https://mlsanity.com/paper/2406.07558/) (2024)
- [Thermal Imaging and Radar for Remote Sleep Monitoring of Breathing and Apnea](https://mlsanity.com/paper/2407.11936/) (2024)

## Abstract (arXiv)

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.