WristO2: Reliable Peripheral Oxygen Saturation Readings from Wrist-Worn Pulse Oximeters
Bibliographic record
Abstract
Peripheral blood oxygen saturation (SpO2) is a vital health signal with many clinical applications. Modern wrist-worn devices, such as the Apple Watch, FitBit, and Samsung Gear, have pulse oximeter sensors, making them theoretically capable of measuring SpO2. However, current techniques for SpO2measurements using pulse oximeter sensors are based on readings taken from the fingertip. Readings collected from the wrist are unreliable and often inaccurate, due to motion and insufficient skin contact. Enabling accurate oxygen saturation monitoring on wearable devices would allow continuous health monitoring and open up new avenues of research. In this work, we explore the reliability of SpO2measurements from the wrist. Using a custom wrist-worn pulse oximeter, we find that existing algorithms used in traditional fingertip SpO2sensors are a poor match for taking measurements from the wrist and can lead to over 90% of readings being inaccurate. We further show that skin tone, IMU sensors, and user-level calibration affect measurement error, and must be considered when designing wrist-worn SpO2sensors and measurement algorithms. Next, based on our findings, we propose WristO2, an alternative approach for reliable SpO2sensing. By selectively pruning unreliable data, WristO2achieves an order of magnitude reduction in error compared to existing algorithms, while still providing sufficiently frequent readings for continuous health monitoring.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.003 |
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".