TY - CONF
T1 - Classification of Recorded Electrooculographic Signals on Drive Activity for Assessing Four Kind of Driver Inattention by Bagged Trees Algorithm: A Pilot Study
AU - Doniec, Rafał
AU - Sieciński, Szymon
AU - Piaseczna, Natalia
AU - Duraj, Konrad
AU - Chwał, Joanna
AU - Gawlikowski, Maciej
AU - Tkacz, Ewaryst
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2023/9/11
Y1 - 2023/9/11
N2 - The act of engaging in secondary activities while driving can cause safety risks on public roads due to the driver’s distracted attention. The objective of the research was to predict changes in driver concentration levels caused by secondary activities (eating, drinking, bending, and turning toward the rear seats) using the electrooculographic (EOG) signal. Four subjects, consisting of one male and three females between the ages of 23 and 57, performed distracting driving activities using a driving simulator. The EOG signals were recorded using JINS MEME Academic Pack smart glasses, and machine learning techniques (boosted trees, bagged trees, subspace discriminant, subspace KNN, RUSBoosted Trees) were used to classify the occurrence of secondary activities. The results show that the highest accuracy (87%) has been achieved for the bagged tree (ensemble classifier).
AB - The act of engaging in secondary activities while driving can cause safety risks on public roads due to the driver’s distracted attention. The objective of the research was to predict changes in driver concentration levels caused by secondary activities (eating, drinking, bending, and turning toward the rear seats) using the electrooculographic (EOG) signal. Four subjects, consisting of one male and three females between the ages of 23 and 57, performed distracting driving activities using a driving simulator. The EOG signals were recorded using JINS MEME Academic Pack smart glasses, and machine learning techniques (boosted trees, bagged trees, subspace discriminant, subspace KNN, RUSBoosted Trees) were used to classify the occurrence of secondary activities. The results show that the highest accuracy (87%) has been achieved for the bagged tree (ensemble classifier).
UR - https://www.mendeley.com/catalogue/68bdecf3-d646-3a4b-909f-02fc88851dd4/
U2 - 10.1007/978-3-031-38430-1_18
DO - 10.1007/978-3-031-38430-1_18
M3 - Conference Papers
SP - 225
EP - 236
ER -
