Driver Drowsiness Classification Using Data Fusion of Vehicle-based Measures and ECG Signals

Sadegh Arefnezhad, Arno Eichberger, Matthias Frühwirth, Clemens Kaufmann, Maximilian Moser

Publikation: KonferenzbeitragPaperForschungBegutachtung

Abstract

Reduced alertness due to the drowsy state that
impairs driving performance has been reported to be one of the
significant causes of road accidents. The aim of this paper is to
present a data fusion of vehicle-based ECG signals for classifying
three levels of driver drowsiness, including alert, moderately
drowsy, and extremely drowsy. Lateral deviation from the road
centerline, steering wheel angle, and lateral acceleration are
employed as vehicle-based signals. Two ECG leads are also
exploited to collect heart rate variability of drivers. Thirty-nine
features from vehicle-based data and ten features from heart rate
variability signals are extracted. Finally, k-nearest neighbors and
random forest are used as classifiers to classify the level of
drowsiness using selected features by the sequential feature
selector. Age and gender, as the two most effective human factors,
are considered to assess the performance of the method in
different age/gender groups. The proposed method is evaluated on
experimental data that were collected from 93 manual driving
tests using 47 different human volunteers in a driving simulator.
Results show that hyperparameter-optimized random forests
obtain an accuracy of 82.8% for the detection of drowsiness levels
based on vehicle signals only, and an accuracy of 88.5% based on
ECG derived data only. Data fusion of ECG signals and vehicle
data improves the accuracy of classification to 91.2%. The model
performs slightly better on older than on younger drivers, but no
gender difference was found.
Originalspracheenglisch
Seitenumfang6
PublikationsstatusEingereicht - 14 Apr 2020
VeranstaltungIEEE SMC 2020;
IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS
- Toronto, Kanada
Dauer: 11 Okt 202014 Okt 2020

Konferenz

KonferenzIEEE SMC 2020;
IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS
LandKanada
OrtToronto
Zeitraum11/10/2014/10/20

Schlagwörter

    ASJC Scopus subject areas

    • Fahrzeugbau

    Fields of Expertise

    • Mobility & Production

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  • Projekte

    Vehicle Dynamics

    Koglbauer, I. V., Lex, C., Shao, L., Semmer, M., Rogic, B., Peer, M., Hackl, A., Sternat, A. S., Schabauer, M., Samiee, S., Eichberger, A., Ager, M., Malić, D., Wohlfahrter, H., Scherndl, C. & Magosi, Z. F.

    1/01/11 → …

    Projekt: Arbeitsgebiet

    Dieses zitieren

    Arefnezhad, S., Eichberger, A., Frühwirth, M., Kaufmann, C., & Moser , M. (2020). Driver Drowsiness Classification Using Data Fusion of Vehicle-based Measures and ECG Signals. Beitrag in IEEE SMC 2020;
    IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, Toronto, Kanada.