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International Journal of Automotive Technology
Erschienen in:

18.06.2024 | Connected Automated Vehicles and ITS, Electrical and Electronics, Human Factors and Ergonomics

Harnessing Electrocardiography Signals for Driver State Classification Using Multi-layered Neural Networks

verfasst von: Amir Tjolleng, Kihyo Jung

Erschienen in: International Journal of Automotive Technology | Ausgabe 2/2025

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Abstract

Driving under conditions of cognitive overload or drowsiness poses serious safety risks and is recognized as a major cause of vehicle collisions. Thus, timely detection of the driver’s state is crucial for preventing accidents. This study proposed the utilization of electrocardiography (ECG) data in conjunction with multi-layered neural network (MNN) models to determine the driver’s state. ECG signals were obtained from 67 participants during simulated driving scenarios that induced either cognitive load or drowsiness. The study considered five driver states: drowsiness, fighting-off drowsiness, normal, medium cognitive load, and high cognitive load. Statistical analysis revealed significant changes in ECG measurements as the driver’s attentiveness levels varied from low (drowsiness) to high (cognitive overload). Multiple MNN models were developed to address individual variations in heart response and achieved classification accuracies exceeding 95%. These findings demonstrated the potential of ECG signal utilization for driver’s state detection to prevent vehicle accidents.
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Metadaten
Titel
Harnessing Electrocardiography Signals for Driver State Classification Using Multi-layered Neural Networks
verfasst von
Amir Tjolleng
Kihyo Jung
Publikationsdatum
18.06.2024
Verlag
The Korean Society of Automotive Engineers
Erschienen in
International Journal of Automotive Technology / Ausgabe 2/2025
Print ISSN: 1229-9138
Elektronische ISSN: 1976-3832
DOI
https://doi.org/10.1007/s12239-024-00109-4

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