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http://dx.doi.org/10.7232/JKIIE.2014.40.3.325

Development of an Evaluation Method for a Driver's Cognitive Workload Using ECG Signal  

Hong, Wongi (Integrated Logistics Support R&D Lab, LIG Nex1)
Lee, Wonsup (Department of Industrial and Management Engineering, Pohang University of Science and Technology)
Jung, Kihyo (School of Industrial Engineering, University of Ulsan)
Lee, Baekhee (Department of Industrial and Management Engineering, Pohang University of Science and Technology)
Park, Jangwoon (Department of Industrial and Management Engineering, Pohang University of Science and Technology)
Park, Suwan (Public and Original Technology Research Center, DGIST)
Park, Yunsuk (Public and Original Technology Research Center, DGIST)
Son, Joonwoo (Public and Original Technology Research Center, DGIST)
Park, Seikwon (Department of Industrial Engineering, Air Force Academy)
You, Heecheon (Department of Industrial and Management Engineering, Pohang University of Science and Technology)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.40, no.3, 2014 , pp. 325-332 More about this Journal
Abstract
High cognitive workload decreases a driver's ability of judgement and response in traffic situation and could result in a traffic accident. Electrocardiography (ECG) has been used for evaluation of drivers' cognitive workload; however, individual differences in ECG response corresponding to cognitive workload have not been fully considered. The present study developed an evaluation method of individual driver's cognitive workload based on ECG data, and evaluated its usefulness through an experiment in a driving simulator. The evaluation method developed by the present study determined the optimal ECG evaluation condition for individual participant by analysis of area under the receiver operating characteristic curve (AUC) for various conditions (total number of conditions = 144) in terms of four aspects (ECG measure, window span, update rate, and workload level). AUC analysis on the various conditions showed that the optimal ECG evaluation condition for each participant was significantly different. In addition, the optimal ECG evaluation condition could accurately detect changes in cognitive workload for 47% of the total participants (n = 15). The evaluation method proposed in the present study can be utilized in the evaluation of individual driver's cognitive workload for an intelligent vehicle.
Keywords
Cognitive Workload; Individual Difference; Area Under the Receiver Operating Characteristic Curve (AUC); Electrocardiography (ECG); Update Rate; Window Span;
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Times Cited By KSCI : 5  (Citation Analysis)
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