Automated 20-20-20 Timers Using Facial Detection and Facial Recognition
Bibliographic record
Abstract
This paper proposes two automated 20-20-20 timers using facial detection and facial recognition respectively. Computer Vision Syndrome (CVS) is a common phenomenon these days, resulting in eye irritation and headaches. A possible prevention of CVS is adopting the 20-20-20 rule. Two types of 20-20-20 timer exist in the market, but both have scopes of improvement. While one type of timer needs to be manually set by the user like a stopwatch, the other type of timer detects the ON -screen time of monitors as a parameter. Both these models fail to detect whether the user is actually looking at the screen, and interrupt the work schedule of the users. The authors have developed a smart automated 20-20-20 timer that uses haar-cascade classifiers for detecting the face and accordingly adapt to the situation. This timer is able to detect the facial features in real time, and alert the user after 20 minutes of screen time. Furthermore, the authors have developed another smart 20-20-20 timer using YOLOv5s. This timer can recognise multiple users looking at the screen and run the timer independently for each person. While the haar-cascade enabled timer is preferred for homes and personal laptops, desktops and PCs; the YOLOv5s enabled timer is suited for offices, schools, colleges and libraries. This paper proposes an automated smart 20-20-20 timer system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".