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July 2, 2026SensorsOpen Access

Emotion-Aware Contextual Modelling for Robust Driver Fatigue Detection

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Authors

SBSebastian BudzanRWRoman Wyżgolik

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Overview

Randomized trial combines emotional and behavioral data for improved driver fatigue detection accuracy, suggesting enhanced safety measures.

Key Points

  • This research aims to develop a robust framework for detecting driver fatigue by integrating behavioral and emotional data.
  • Implemented a context-aware framework using facial landmarks and emotional information.
  • Applied multi-stage validation combining eye closure detection, mouth activity, and head pose analysis.
  • Utilized EfficientNet-B0 for emotion recognition based on the AffectNet dataset.
  • Achieved 94% accuracy on the NTHU-DDD dataset.
  • Demonstrated improved robustness in detecting fatigue under non-frontal head poses.
  • Provided interpretable estimates of driver state beyond simple classifications.

Cite This Study

Budzan et al. (2026) studied this question.

synapsesocial.com/papers/6a4600a29ed1343031310b1bhttps://doi.org/10.3390/s26134120
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