Driver Fatigue Detection Using Measures of Heart Rate Variability and Electrodermal Activity
Bibliographic record
Abstract
This paper investigated the feasibility and reliability of employing various physiological measures -for determining drivers’ fatigue levels, which may ultimately lead to a solution for real-time detection of driver fatigue state for improving driving and traffic safety. An experimental study was conducted to collect the data, including fatigue levels assessed via the Karolinska sleepiness scale and heart rate variability (HRV) and electrodermal activity (EDA) features. Based on an extensive statistical analysis of the collected data, significant differences in numerous HRV and EDA features were found across varying fatigue levels. Employing several machine learning techniques for classification purposes, the most favorable binary classification performance was achieved using the Light Gradient Boosting Machine classifier, with an accuracy rate of 88.7% when HRV and EDA features were utilized as inputs. Meanwhile, for three-class classification, the accuracy decreased slightly to 85.6% when employing the Random Forest classifier. These outcomes underscore the potential of HRV and EDA feature fusion in capturing diverse physiological responses to fatigue, thereby bolstering fatigue detection performance. Besides, subject-independent classification yielded an accuracy of 52.0% and 53.3%, reflecting the potential bias introduced by unobserved heterogeneity in classification models. Moreover, feature selection should be prioritized over dimensionality reduction in feature fusion endeavors to diminish feature redundancy and prevent information loss. The findings of this study could contribute to the development of reliable driver fatigue detection methodologies utilizing readily available measures of physiological response measures, such as HRV and EDA features.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".