Bibliographic record
Abstract
Abstract Introduction The unobtrusive measurement of sleep psychophysiology is fundamental to understanding how we sleep. Our current understanding of sleep psychophysiology is still limited, due to the fact that conventional measures are disruptive and impractical to measure natural sleep. This pilot study examined sleep using a novel method of completely contactless psychophysiological monitoring called infrared-video photoplethysmography (IR-VPPG) in comparison with the conventional gold standard sleep physiological (e.g., ECG) monitoring. We predicted that IR-VPPG measurements of psychophysiology would demonstrate high accuracy and strong agreement with conventional measures of psychophysiology specifically heart rate. Methods We assessed 9 adult participants (Mage = 25.8 +/-4.1) during a 90-minute nap in a controlled lab environment. We monitored participants during their nap using specialized infrared recording, ECG and respiratory monitoring. Using specialized video processing and machine learning models we extracted a wide range of psychophysiological activities including Heart Rate from the participants video data. We then contrasted this data to the conventional sleep physiological measures. Results Comparison of heart rate between methods demonstrated a high accuracy of 96.49%, a low error of 3.51%, and high agreement (Mdiff = -0.99 +/- 2.77) between our novel IRPPG and the conventional ECG monitoring. Conclusion This pilot study provides evidence for a revolutionary contactless method, IRPPG to accurately monitor sleep psychophysiology, notably heart rate naturalistically. We are currently testing large sample while expanding the use cases of IRVPPG providing additional physiological measurements and assessing longitudinal use cases. Ultimately we hope to provide this contactless tool to users, researchers, and clinicians to monitor and measure psychophysiological features during sleep. Potential applications of this technology in research and other fields are discussed. Support (if any)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".