Video Analytic Based Health Monitoring for Driver in Moving Vehicle by Extracting Effective Heart Rate Inducing Features
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We propose a novel remote heart rate (HR) estimation method using facial images based on video analytics. Most of previous methods have been demonstrated in well-controlled indoor environments. In contrast, this paper proposes a practical video analytic framework under actual driving conditions by extracting key HR inducing features. In particular, when cars are driven, effective and stable HR estimation becomes challenging as there are many dynamic elements, such as rapid illumination changes, vibrations, and ambient lighting that can exist in the vehicle interior. To overcome those disturbances of HR estimation, the driver face region is first detected and cropped to the region of interest (RoI). Second, the components related to HR are extracted from mixed noisy components using ensemble empirical mode decomposition (EEMD). Finally, the extracted signal is analyzed in frequency domain and smoothed with temporal filtering. To verify our approach, the proposed method is compared with recent prominent methods employing a public HCI dataset. It has been demonstrated that the proposed approach delivers superior performance under driving conditions using Bland-Altman plots.
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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 | 0.001 | 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 | 0.001 |
| 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 it