Implementing Video Plethysmography for Contactless Vital Signs Measurement in Perioperative Care
Bibliographic record
Abstract
Contactless vital signs measurement is the next step in the advancement of virtual care to enhance preoperative assessment and postoperative monitoring. Remote video plethysmography (VPPG) can measure blood pressure (BP) and heart rate (HR) using a facial video. VPPG utilizes light information reflected from a subject’s face, captured by the camera optical sensors, then analyzes it using advanced machine learning to measure changes in blood flow. This thesis aims to assess the accuracy and feasibility of implementing VPPG for BP and HR measurements in perioperative care. Our proof-of-concept study in surgical patients showed that VPPG predicted HR with high accuracy (95%) and BP with moderate accuracy (88%) compared to Welch-Allyn standard medical device. Over 90% of participants reported high satisfaction and 80% would recommend the use of contactless vital signs using VPPG in future clinical setting.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".