naa a22 4500 510823858 CHVBK 20180411083547.0 cr unu---uuuuu 180411e20130801xx s 000 0 eng 10.1007/s12541-013-0179-7 doi (NATIONALLICENCE)springer-10.1007/s12541-013-0179-7 Identification of driver cognitive workload using support vector machines with driving performance, physiology and eye movement in a driving simulator [Elektronische Daten] [Joonwoo Son, Hosang Oh, Myoungouk Park] This paper suggests experimental approaches for identifying driver's cognitive workload using support vector machines (SVMs) with driving performance, physiological response and eye movement data. In order to construct a classification model for detecting high cognitive workload condition, driving simulation experiments were conducted. For the experiments, 30 participants (15 younger males in the 25-35 age range (M = 27.9, SD = 3.13) and 15 older males in the 60-69 (M = 63.2, SD = 1.74)) drove a simulated highway in a fixed-base driving simulator. While driving through 37 km of straight highway, participants conducted three levels of cognitive secondary tasks, i.e. an auditory delayed digit recall task, at specified segments for 10 minutes and their driving performance, physiological response and eye movement data were collected. In this study, the model performances with different combination of measures were assessed with the nested cross-validation method. As a result, it was demonstrated that the proposed SVM models were able to identify driver's cognitive workload with high accuracy. The best performance was achieved with a combination of the standard deviation of lane position (SDLP), physiology and gaze information. The best model obtained 89.0% accuracy, sensitivity of 86.4% and specificity of 91.7%. Korean Society for Precision Engineering and Springer-Verlag Berlin Heidelberg, 2013 Driver state estimation nationallicence Cognitive workload nationallicence Support vector machines nationallicence Intelligent vehicle nationallicence Driving simulator nationallicence Son Joonwoo DGIST HumanLab, 50-1 Sang-Ri Hyeonpung-Myeon, Dalseong-Gun, 711-873, Daegu, South Korea aut Oh Hosang DGIST HumanLab, 50-1 Sang-Ri Hyeonpung-Myeon, Dalseong-Gun, 711-873, Daegu, South Korea aut Park Myoungouk DGIST HumanLab, 50-1 Sang-Ri Hyeonpung-Myeon, Dalseong-Gun, 711-873, Daegu, South Korea aut International Journal of Precision Engineering and Manufacturing Springer Berlin Heidelberg 14/8(2013-08-01), 1321-1327 2234-7593 14:8<1321 2013 14 12541 https://doi.org/10.1007/s12541-013-0179-7 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s12541-013-0179-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Son Joonwoo DGIST HumanLab, 50-1 Sang-Ri Hyeonpung-Myeon, Dalseong-Gun, 711-873, Daegu, South Korea aut NATIONALLICENCE 700 1- Oh Hosang DGIST HumanLab, 50-1 Sang-Ri Hyeonpung-Myeon, Dalseong-Gun, 711-873, Daegu, South Korea aut NATIONALLICENCE 700 1- Park Myoungouk DGIST HumanLab, 50-1 Sang-Ri Hyeonpung-Myeon, Dalseong-Gun, 711-873, Daegu, South Korea aut NATIONALLICENCE 773 0- International Journal of Precision Engineering and Manufacturing Springer Berlin Heidelberg 14/8(2013-08-01), 1321-1327 2234-7593 14:8<1321 2013 14 12541 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer