Normalization is All You Need: Robust Full-Range Contactless SpO2 Estimation Across Users

The accurate estimation of peripheral capillary oxygen saturation (SpO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) is vital for monitoring respiratory health, with applications spanning medical diagnostics and fitness tracking. Remote photoplethysmography (rPPG) offers a convenient and non-contact approach for SpO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> estimation. However, existing methods predominantly rely on data within the normal SpO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> range, hindering their effectiveness during hypoxemia. Moreover, cross-user variations poses significant challenges for practicality. To address these limitations, we propose a simple yet effective normalization-based SpO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> estimation algorithm. By aligning individual Ratio-of-Ratios (RoR) data with a standard model at the matching SpO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> level, we mitigate cross-user variation, accommodate different camera configurations, and account for lighting changes. Our experiments demonstrate that the proposed method achieves an rMSE of 2.8% with leave-one-subject-out cross-validation across the full SpO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> range (70%-100%), significantly outperforming existing RoR-based and CNN-based SpO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> estimation approaches. Notably, our methods excel in accurately identifying hypoxemia, a critical clinical requirement. We anticipate broader applicability of our approach in rPPG-based vital sign monitoring, underlining the potential for enhancing robustness and reliability in various domains.