A Deep Learning-Based Remote Plethysmography with the Application in Monitoring drivers’ Wellness
Bibliographic record
Abstract
In this paper we proposed a long-short term memory (LSTM) based prediction approach, called LBPA, for vital sign assessment using facial videos. The objective is to create a remote photoplethysmography (rPPG) technology which estimates the heart rate from camera, and can be utilized in driver state monitoring. Our methodology processes the facial videos to obtain accurate heart rate estimation for each frame. We evaluated the performance of our proposed method on the RGB videos from the COHFACE database and compared to the traditional machine learning techniques. The results demonstrate that the accuracy of our proposed LBPA outperforms the handcrafted approaches. The superior performance and the low error rate achieved by the LBPA (1.98 MAE and 2.88% MAPE) indicate its potential to serve as an effective tool for remote heart rate estimation, with the potential to contribute to the development of more accurate and reliable systems in monitoring the physiological state of the drivers for safety purposes.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".