A Review of Photoplethysmography-based Physiological Measurement and Estimation, Part 2: Multi-input Methods
Bibliographic record
Abstract
Photoplethysmography can be used to estimate many physiological parameters based on features extracted from the measured waveform. Following the single parameter estimations that have been reviewed in part 1 of this paper, we here review methods where the waveform is used in conjunction with other measured physiological signals. Being a low-cost, non-invasive, and user friendly technique, many PPG-based physiological data extraction methods are being researched. The parameters reviewed that can be estimated using the PPG waveform plus additional inputs include cardiac output, blood pressure, venous function assessment, blood oxygen saturation, and fetal heart rate and fetal oxygen saturation. The different processing techniques, algorithms and methods are reviewed in addition to providing a comparison of results with the reference standards to validate the different methods. Future research considerations for each parameter estimation are also discussed. This paper could be helpful for future research on PPG based wearable devices for physiological multi-parameter estimations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".