Driver Monitoring of Automated Vehicles by Classification of Driver Drowsiness using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals

Sadegh Arefnezhad*, Arno Eichberger, Matthias Frühwirth, Clemens Kaufmann, Maximilian Moser , Ioana Victoria Koglbauer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Driver drowsiness is one of the leading causes of traffic accidents. This paper proposes a new method for classifying driver drowsiness using deep convolution neural networks trained by wavelet scalogram images of electrocardiogram (ECG) signals. Three different classes were defined for drowsiness based on video observation of driving tests performed in a simulator for manual and automated modes. The Bayesian optimization method is employed to optimize the hyperparameters of the designed neural networks, such as the learning rate and the number of neurons in every layer. To assess the results of the deep network method, heart rate variability (HRV) data is derived from the ECG signals, some features are extracted from this data, and finally, random forest and k-nearest neighbors (KNN) classifiers are used as two traditional methods to classify the drowsiness levels. Results show that the trained deep network achieves balanced accuracies of about 77% and 79% in the manual and automated modes, respectively. However, the best obtained balanced accuracies using traditional methods are about 62% and 64%. We conclude that designed deep networks working with wavelet scalogram images of ECG signals significantly outperform KNN and random forest classifiers which are trained on HRV-based features.
Original languageEnglish
Article number480
Number of pages25
JournalEnergies
Volume15
Issue number2
DOIs
Publication statusPublished - 10 Jan 2022

Keywords

  • Convolutional neural network
  • Driver drowsiness
  • ECG signals
  • Heart rate variability
  • Wavelet scalogram
  • ECG signal

ASJC Scopus subject areas

  • Automotive Engineering
  • Control and Optimization
  • Energy (miscellaneous)
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Fuel Technology
  • Renewable Energy, Sustainability and the Environment

Fields of Expertise

  • Mobility & Production

Treatment code (Nähere Zuordnung)

  • Application

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