Scipediacontent: Scipediacontent moved page Draft Content 805210973 to 238,295pf

2021-03-23T03:09:17Z

Scipediacontent moved page Draft Content 805210973 to 238,295pf

Scipediacontent: Created page with " == Abstract == Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving autom..."

2021-03-23T03:07:12Z

Created page with " == Abstract == Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving autom..."

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== 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.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_805210973-beopen1422-7771-document.pdf</pdf>

== Original document ==

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

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

* [http://europepmc.org/articles/PMC7070962 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/pdf]

* [https://www.mdpi.com/1424-8220/20/4/1029 https://www.mdpi.com/1424-8220/20/4/1029],
: [https://doaj.org/toc/1424-8220 https://doaj.org/toc/1424-8220] under the license cc-by

* [https://www.mdpi.com/1424-8220/20/4/1029/pdf https://www.mdpi.com/1424-8220/20/4/1029/pdf],
: [http://dx.doi.org/10.3390/s20041029 http://dx.doi.org/10.3390/s20041029]

* [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070962 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070962],
: [https://dblp.uni-trier.de/db/journals/sensors/sensors20.html#KundingerSR20 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],
: [https://www.mdpi.com/1424-8220/20/4/1029/pdf https://www.mdpi.com/1424-8220/20/4/1029/pdf],
: [https://doi.org/10.3390/s20041029 https://doi.org/10.3390/s20041029],
: [https://academic.microsoft.com/#/detail/3006499155 https://academic.microsoft.com/#/detail/3006499155] under the license https://creativecommons.org/licenses/by/4.0/

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Scipediacontent: Scipediacontent moved page Draft Content 805210973 to 238,295pf

Scipediacontent: Created page with " == Abstract == Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving autom..."