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 and Ioana Victoria Koglbauer
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Sadegh Arefnezhad: Institute of Automotive Engineering, Faculty of Mechanical Engineering and Economic Sciences, Graz University of Technology, 8010 Graz, Austria
Arno Eichberger: Institute of Automotive Engineering, Faculty of Mechanical Engineering and Economic Sciences, Graz University of Technology, 8010 Graz, Austria
Matthias Frühwirth: Human Research Institute of Health Technology and Prevention Research, Franz-Pichler-Strasse 30, 8160 Weiz, Austria
Clemens Kaufmann: Apptec Ventures Factum, Slamastrasse 43, 1230 Vienna, Austria
Maximilian Moser: Human Research Institute of Health Technology and Prevention Research, Franz-Pichler-Strasse 30, 8160 Weiz, Austria
Ioana Victoria Koglbauer: Institute of Automotive Engineering, Faculty of Mechanical Engineering and Economic Sciences, Graz University of Technology, 8010 Graz, Austria

Energies, 2022, vol. 15, issue 2, 1-25

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.

Keywords: convolutional neural network; driver drowsiness; ECG signal; heart rate variability; wavelet scalogram (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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