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Drivers' Mental Engagement Analysis Using Multi-Sensor Fusion Approaches Based on Deep Convolutional Neural Networks.

Taraneh Aminosharieh Najafi1, Antonio Affanni1, Roberto Rinaldo1

  • 1Polytechnic Department of Engineering and Architecture, University of Udine, Via Delle Scienze 206, 33100 Udine, Italy.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary
This summary is machine-generated.

This study assessed driver mental engagement using physiological signals like Electroencephalogram (EEG), Skin Potential Response (SPR), and Electrocardiogram (ECG) during simulated driving. Sensor fusion with deep learning achieved 82.0% accuracy in distinguishing engagement levels.

Keywords:
deep convolutional neural networkdrivers’ mental engagementelectrocardiogramelectrodermal activityelectroencephalogramsensor fusion

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Area of Science:

  • Human-Computer Interaction
  • Neuroscience
  • Automotive Engineering

Background:

  • Assessing driver mental engagement is crucial for automotive safety, especially with increasing automation.
  • Physiological signals offer objective measures of cognitive and affective states.
  • Sensor fusion techniques can enhance the accuracy of engagement detection by integrating multimodal data.

Purpose of the Study:

  • To evaluate driver mental engagement states in manual and autonomous driving scenarios.
  • To develop and compare two deep learning-based sensor fusion architectures for engagement detection.
  • To investigate the effectiveness of combining Electroencephalogram (EEG), Skin Potential Response (SPR), and Electrocardiogram (ECG) signals.

Main Methods:

  • Utilized a driving simulator with participants instrumented with EEG, SPR, and ECG sensors.
  • Developed a custom Graphical User Interface (GUI) for real-time physiological signal recording and synchronization.
  • Implemented two deep Convolutional Neural Network (ConvNet) architectures for sensor fusion, including a novel multi-branch approach.
  • Employed Leave-One-Subject-Out (LOSO) cross-validation for model evaluation.

Main Results:

  • The proposed multi-branch ConvNet architecture integrating EEG, SPR, and ECG signals achieved the highest accuracy of 82.0%.
  • Sensor fusion, particularly at the feature level, significantly improved the ability to discern driver mental engagement compared to using EEG alone.
  • The study demonstrated the effectiveness of the developed deep learning models in analyzing multimodal physiological data.

Conclusions:

  • Integrating multiple physiological signals (EEG, SPR, ECG) via advanced deep learning architectures enhances the accuracy of driver mental engagement detection.
  • The novel multi-branch fusion approach shows promise for real-world applications in driver monitoring systems.
  • This research contributes to safer driving environments by providing a robust method for assessing driver cognitive states.