Towards using the Yerkes-Dodson Law to select optimal training difficulty in firefighter simulations: A machine learning based ECG approach
Bibliographic record
Abstract
First responders are frequently confronted with highly stressful and hazardous environments.Frequent training of first responders is required to optimize performance during emergencies and ensure rational decision-making during deployment.To allow easy access to training, serious games with automatically generated scenarios are being used.Stress can be used as a powerful tool to assess the difficulty of an automatically generated scenario for the individual using the Yerkes-Dodson Law.In this paper, a system is proposed to determine a quantitative value about a user's stress level without the need for a baseline recording.Different machine learning models are trained based on the SWELL-KW dataset using heart rate variability (HRV) metrics.Two approaches are examined: A threeclass approach that classifies the stress into "low", "medium", or "high", and a scale approach that classifies the stress into a scale from 0 to 10.The results are compared using a histogram of absolute errors to gain further insight into the models' performances due to the ordinal structure of the ground truth.The results are promising and show a maximum accuracy of 90.7% and 35.2% for the three -class approach and the scale approach, respectively.
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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 | 0.002 |
| 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 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".