Enhancing Pulse Oximetry Accuracy with Personal Parameter Integration in Wearable Devices
Bibliographic record
Abstract
Pulse oximetry is a standard method for noninvasive clinical monitoring of peripheral oxygen saturation (SpO2), a key vital sign, yet studies highlight its reduced accuracy in individuals with darker skin pigmentation, often resulting in occult hypoxemia. This discrepancy arises from calibration biases and the optical effects of elevated melanin levels, which interfere with light transmission and absorption. To address this disparity, we propose a lightweight, machine learning-based correction model that estimates arterial oxygen saturation from SpO2readings by incorporating demographic and skin pigmentation metrics. Leveraging the OpenOximetry dataset and deploying compact neural networks, we trained and optimized a model suitable for real-time operation on resource-constrained edge hardware. The model was quantized using TensorFlow Lite to minimize memory and computation requirements. Deployment and testing on a Raspberry Pi 5 confirmed feasibility, with the quantized model achieving an RMSE of$\mathbf{2. 1 0 3 \%}$and$R^{2}$of$\mathbf{0. 9 4 7}$while requiring 43.5 KB of RAM and 122 ms inference time. Our approach demonstrates a viable path to bias-aware, accurate oxygen monitoring by enabling demographically informed SpO2correction on edge devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".