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Open AccessArticle

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

1
AUDI AG, 85045 Ingolstadt, Germany
2
Faculty of Computer Science, Technische Hochschule Ingolstadt (THI), 85049 Ingolstadt, Germany
3
Department of Computer Science, Johannes Kepler University (JKU), 4040 Linz, Austria
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 1029; https://doi.org/10.3390/s20041029
Received: 10 January 2020 / Revised: 10 February 2020 / Accepted: 10 February 2020 / Published: 14 February 2020
(This article belongs to the Special Issue Human-Machine Interaction and Sensors)
Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving. View Full-Text
Keywords: drowsiness detection; driver state; simulator study; physiological measures; machine learning; wearable sensors; automated driving drowsiness detection; driver state; simulator study; physiological measures; machine learning; wearable sensors; automated driving
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MDPI and ACS Style

Kundinger, T.; Sofra, N.; Riener, A. Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection. Sensors 2020, 20, 1029.

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