Eye-Tracking-Based Risk Perception Prediction for Adaptive Hazard Recognition Training
Bibliographic record
Abstract
Hazard recognition is a critical cognitive skill that directly impacts construction site safety, yet conventional safety training methods often fail to account for individual differences in risk perception.This study explores the application of eye-tracking-based biosignal analysis for real-time risk perception assessment in adaptive virtual reality (VR) training.By capturing eye-tracking metrics such as fixation duration, gaze velocity, and pupil dilation, we developed a machine learning model to classify workers' risk perception levels.Among the evaluated classifiers, Gradient Boosting achieved the highest performance (accuracy = 76.81%,AUC = 0.81), demonstrating superior discrimination between high and low-risk perception groups.Compared to traditional physiological signals like EEG, EDA, and PPG, eye-tracking provides a non-invasive, real-time alternative for monitoring hazard recognition.These findings highlight the potential of integrating eye-tracking with adaptive VR training to improve risk perception assessment, which could inform personalized safety interventions in future research and applications.Further studies should validate these results in larger, more diverse cohorts and refine adaptive training algorithms for realworld implementation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".