Evaluating a Camera-Based Approach to Assess Cognitive Load During Manufacturing Computer Tasks
Bibliographic record
Abstract
Suboptimal levels of cognitive load have been shown to lead to distractions, stress, and physical injuries in work environments. Yet, traditional methods for measuring cognitive load present known logistical and methodological issues: while self-reported measures suffer from poor construct validity, physiological measures often require expensive instruments and time-consuming calibration. In recent years, research has linked blink rate (i.e., the number of eye blinks per minute) with cognitive load, showing a higher blink rate with increased load. Despite this, scientific-grade eye trackers are usually expensive and invasive, making them unsuitable for work environments. In this study, we aimed to evaluate the accuracy of a camera-based approach to measure blink rate using a widely available generic webcam. To test this, we employed two tasks that resemble computer tasks common in office and manufacturing settings. Our results showed that the camera-based approach measured cognitive load as accurately as a scientific-grade eye tracker. These findings are crucial as they provide an affordable alternative to expensive and invasive instruments for measuring cognitive load in the workplace.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".