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 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".