Physiological parameter monitoring of drivers based on video data and independent vector analysis
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
Although modern cars are equipped with advanced technologies to be faster, more comfortable and safer, one essential piece of the driving system, the driver, is missing in the picture. Among the physiological measures used for wellness purposes, heart rate variability has been shown to be directly associated with mental and physical status, and is easy to measure. In this paper, to maintain the driver's comfort and enhance the driving safety, we propose a non-contact, video-based approach to continuously monitor the driver's heart rate variability under real-world driving circumstances. Previously, several methods were proposed for similar goals under laboratory conditions, where simple face detectors and independent component analysis approach were used, and they may fail in both image understanding and signal processing steps under real-world circumstances in driving. Here we propose using advanced facial landmark and pose estimation, and independent vector analysis to extract heart rate variability. Our preliminary experimental results demonstrated that the proposed approach works better than the previous state-of-arts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it