Latest revision as of 04:09, 23 March 2021

Abstract

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 (&gt

92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human&ndash

machine interaction in a car and especially for driver state monitoring in the field of automated driving.

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The different versions of the original document can be found in:

• http://dx.doi.org/10.3390/s20041029 under the license https://creativecommons.org/licenses/by

• http://europepmc.org/articles/PMC7070962 under the license http://creativecommons.org/licenses/by/3.0/

• https://www.mdpi.com/1424-8220/20/4/1029/pdf

• https://www.mdpi.com/1424-8220/20/4/1029,

https://doaj.org/toc/1424-8220 under the license cc-by

• https://www.mdpi.com/1424-8220/20/4/1029/pdf,

http://dx.doi.org/10.3390/s20041029

• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070962,

https://dblp.uni-trier.de/db/journals/sensors/sensors20.html#KundingerSR20,

https://www.mdpi.com/1424-8220/20/4/1029,

https://www.mdpi.com/1424-8220/20/4/1029/pdf,

https://doi.org/10.3390/s20041029,

https://academic.microsoft.com/#/detail/3006499155 under the license https://creativecommons.org/licenses/by/4.0/