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Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving.

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This study introduces a noninvasive system to detect driver stress using physiological signals like Skin Potential Response (SPR) and electrocardiogram (ECG) during simulated traffic. The findings confirm the system

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Area of Science:

  • Physiological computing
  • Human-computer interaction
  • Transportation psychology

Background:

  • Assessing driver stress is crucial for road safety and understanding human behavior in traffic.
  • Traditional methods for stress detection can be invasive or subjective.
  • Simulated driving environments offer a controlled setting to study physiological responses to traffic.

Purpose of the Study:

  • To develop and validate a noninvasive system for automatically assessing driver stress levels.
  • To analyze the impact of different traffic conditions (with and without traffic) on driver physiological responses.
  • To investigate the efficacy of Skin Potential Response (SPR) and Electrocardiogram (ECG) signals in stress detection.

Main Methods:

  • Utilized a driving simulator with a moving platform to record physiological signals (SPR and ECG) from participants.
  • Applied a Motion Artifact (MA) removal algorithm to clean the SPR signals.
  • Extracted statistical features from cleaned SPR and ECG signals for analysis.
  • Employed binary Machine Learning (ML) models to classify stress levels based on signal features and scalogram analysis.

Main Results:

  • The proposed system successfully identified stress in drivers under different traffic conditions.
  • Analysis of SPR signal scalograms provided insights into stress-related physiological changes.
  • Machine Learning models accurately distinguished between stressful and non-stressful driving scenarios based on physiological data.

Conclusions:

  • The developed noninvasive system demonstrates high applicability for automatic driver stress assessment.
  • Physiological signals, particularly SPR and ECG, are effective indicators of stress in simulated driving.
  • This approach offers a promising tool for enhancing driver safety and monitoring well-being in real-world traffic scenarios.