Wearable Upper Arm SpO<sub>2</sub> Sensor for Wellness Monitoring
Bibliographic record
Abstract
Objective:This paper describes the full development of a sensor for measuring optical heart rate (OHR) and blood oxygen saturation (SpO2).Methods:A wearable sensor with a new type of skin compatible dispensed lens was designed and manufactured. All critical optical components, light emitting diodes (LEDs) and photodiode (PD) were close to skin and gave maximum light intensity due to minimal loss in the lens structure. Lens and optical components formed a thin monolithic structure.Results:Suppressed crosstalk between LED and PD was achieved by using two types of dispensed material: light blocking and transparent. High signal to noise ratio (SNR) and amplitude in the alternating current (AC) part of the photoplethysmography (PPG) signal were achieved. User comfort was achieved by having a small sensor located on the upper arm. When re-training the algorithm from our first iteration, the multiwavelength PPG sensor showed an SpO2RMSE of 2.61% with a 7-second average analysis for 25 participants. The average RMSE of heart rate over all 25 participants was 1.6 ± 1.1%.Conclusion:This study demonstrates a sensor with a clinical grade SpO2measurement and a highly accurate OHR measurement that is also comfortable and easy to wear.Significance:A dispensing method provides a new way of manufacturing sensor elements for wearable sensors with increased performance with reduced crosstalk.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".