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Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals

Aminosharieh Najafi, Taraneh ; Affanni, Antonio ; Rinaldo, Roberto ; Zontone, Pamela

Sensors (Basel, Switzerland), 2023-02, Vol.23 (4), p.2039 [Periódico revisado por pares]

Switzerland: MDPI AG

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  • Título:
    Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals
  • Autor: Aminosharieh Najafi, Taraneh ; Affanni, Antonio ; Rinaldo, Roberto ; Zontone, Pamela
  • Assuntos: Automobile Driving ; blink rate ; driver attention ; Driver behavior ; driving simulator ; Electrocardiogram ; Electrocardiography ; electrodermal activity ; Electrodes ; electroencephalogram ; Electroencephalography ; Evaluation ; Experiments ; Frequencies ; Galvanic Skin Response ; Heart beat ; Heart Rate ; Humans ; Physiological aspects ; Physiology ; Sensors ; Signal processing ; Statistical tests
  • É parte de: Sensors (Basel, Switzerland), 2023-02, Vol.23 (4), p.2039
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    This paper is an extended version of our paper published in “Driver Attention Assessment by Blink Rate Measurement from EEG Signals” in Proceedings of the IEEE International Workshop on Metrology for Automotive (MetroAutomotive), Modena, Italy, 4–6 June 2022.
  • Descrição: In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual and autonomous driving scenarios. Most systems described in the literature use cameras to evaluate features such as blink rate and gaze direction. In this study, we instead analyse the subjects' Electrodermal activity (EDA) Skin Potential Response (SPR), their Electrocardiogram (ECG), and their Electroencephalogram (EEG). From these signals we extract a number of physiological measures, including eye blink rate and beta frequency band power from EEG, heart rate from ECG, and SPR features, then investigate their capability to assess the mental state and engagement level of the test subjects. In particular, and as confirmed by statistical tests, the signals reveal that in the manual scenario the subjects experienced a more challenged mental state and paid higher attention to driving tasks compared to the autonomous scenario. A different experiment in which subjects drove in three different setups, i.e., a manual driving scenario and two autonomous driving scenarios characterized by different vehicle settings, confirmed that manual driving is more mentally demanding than autonomous driving. Therefore, we can conclude that the proposed approach is an appropriate way to monitor driver attention.
  • Editor: Switzerland: MDPI AG
  • Idioma: Inglês
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