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Related Experiment Video

Updated: May 8, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

From Anger Detection to Intensity Modeling: A Two-Stage Machine Learning Approach Using Driving Performance and

Manhua Wang1, Haoyu Teng2, Myounghoon Jeon3

  • 1Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.

IISE Transactions on Occupational Ergonomics and Human Factors
|May 7, 2026
PubMed
Summary

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This summary is machine-generated.

This study shows driver anger intensity can be modeled using driving data and physiological signals. A two-stage machine learning approach accurately detects and classifies driver anger, improving safety for professional drivers.

Area of Science:

  • Occupational Safety and Health
  • Machine Learning Applications
  • Transportation Psychology

Background:

  • Driver anger is a significant safety concern in occupational settings, particularly for commercial drivers.
  • Existing methods for detecting driver anger often lack accuracy and fail to differentiate intensity levels.
  • Frequent exposure to anger-eliciting situations impacts professional drivers' well-being and job performance.

Purpose of the Study:

  • To develop and validate a machine learning framework for modeling driver anger intensity.
  • To improve the accuracy of anger detection and reduce misclassification of neutral states.
  • To explore the occupational applications of driver anger intensity detection for enhanced safety and worker well-being.

Main Methods:

  • A two-stage machine learning framework was employed, first detecting anger presence, then classifying its intensity.
Keywords:
Emotion recognitiondriver monitoring systemsdriving angermachine learningphysiological signal analysisroad rage

Related Experiment Videos

Last Updated: May 8, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Combined driving performance metrics and physiological signals were utilized as input features.
  • Performance was compared against a single-stage four-class model.

    • Main Results:

    • The two-stage model significantly improved accuracy in detecting and classifying driver anger intensity.
    • A substantial reduction in misclassification of neutral emotional states was observed.
    • Feasible modeling of driver anger across three intensity levels was demonstrated.

    Conclusions:

    • Driver anger intensity can be effectively modeled using a combination of driving data and physiological signals.
    • The proposed two-stage machine learning framework offers a more accurate approach to anger detection in occupational drivers.
    • Findings support the integration of anger-intensity detection into driver monitoring systems for adaptive assistance and operational improvements, enhancing fleet safety and driver well-being.