A Novel Method for Classifying Driver Mental Workload Under Naturalistic Conditions With Information From Near-Infrared Spectroscopy

• Published in: Frontiers in human neuroscience Vol. 12; p. 431
• Main Authors: Le, Anh Son, Aoki, Hirofumi, Murase, Fumihiko, Ishida, Kenji
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 26.10.2018; Frontiers Media S.A
• Subjects: artificial intelligence; Automobiles; Brain research; Classification; Cognitive ability; cognitive distraction; driver attention; Eye movements; Feasibility studies; I.R. radiation; Infrared spectroscopy; Intelligence; Learning algorithms; mental workload; Methods; near-infrared spectroscopy; Nearest-neighbor; Neuroscience; Physiology; Researchers; Spectrum analysis; Workloads; Vietnam; Japan; cognitive distraction; driver attention; near-infrared spectroscopy; classification; mental workload; artificial intelligence
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2018.00431

Driver cognitive distraction is a critical factor in road safety, and its evaluation, especially under real conditions, presents challenges to researchers and engineers. In this study, we considered mental workload from a secondary task as a potential source of cognitive distraction and aimed to estimate the increased cognitive load on the driver with a four-channel near-infrared spectroscopy (NIRS) device by introducing a machine-learning method for hemodynamic data. To produce added cognitive workload in a driver beyond just driving, two levels of an auditory presentation n-back task were used. A total of 60 experimental data sets from the NIRS device during two driving tasks were obtained and analyzed by machine-learning algorithms. We used two techniques to prevent overfitting of the classification models: (1) -fold cross-validation and principal-component analysis, and (2) retaining 25% of the data (testing data) for testing of the model after classification. Six types of classifier were trained and tested: decision tree, discriminant analysis, logistic regression, the support vector machine, the nearest neighbor classifier, and the ensemble classifier. Cognitive workload levels were well classified from the NIRS data in the cases of subject-dependent classification (the accuracy of classification increased from 81.30 to 95.40%, and the accuracy of prediction of the testing data was 82.18 to 96.08%), subject 26 independent classification (the accuracy of classification increased from 84.90 to 89.50%, and the accuracy of prediction of the testing data increased from 84.08 to 89.91%), and channel-independent classification (classification 82.90%, prediction 82.74%). NIRS data in conjunction with an artificial intelligence method can therefore be used to classify mental workload as a source of potential cognitive distraction in real time under naturalistic conditions; this information may be utilized in driver assistance systems to prevent road accidents.