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Open AccessArticle

Detection of Drivers’ Anxiety Invoked by Driving Situations Using Multimodal Biosignals

1
Department of Human Factors Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
2
Intelligent Robotics Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
*
Author to whom correspondence should be addressed.
Processes 2020, 8(2), 155; https://doi.org/10.3390/pr8020155
Received: 10 November 2019 / Revised: 11 January 2020 / Accepted: 21 January 2020 / Published: 25 January 2020
(This article belongs to the Special Issue Big Data in Biology, Life Sciences and Healthcare)
It has become increasingly important to monitor drivers’ negative emotions during driving to prevent accidents. Despite drivers’ anxiety being critical for safe driving, there is a lack of systematic approaches to detect anxiety in driving situations. This study employed multimodal biosignals, including electroencephalography (EEG), photoplethysmography (PPG), electrodermal activity (EDA) and pupil size to estimate anxiety under various driving situations. Thirty-one drivers, with at least one year of driving experience, watched a set of thirty black box videos including anxiety-invoking events, and another set of thirty videos without them, while their biosignals were measured. Then, they self-reported anxiety-invoked time points in each video, from which features of each biosignal were extracted. The logistic regression (LR) method classified single biosignals to detect anxiety. Furthermore, in the order of PPG, EDA, pupil, and EEG (easiest to hardest accessibility), LR classified accumulated multimodal signals. Classification using EEG alone showed the highest accuracy of 77.01%, while other biosignals led to a classification with accuracy no higher than the chance level. This study exhibited the feasibility of utilizing biosignals to detect anxiety invoked by driving situations, demonstrating benefits of EEG over other biosignals.
Keywords: driver anxiety; multimodal biosignals; emotion detection driver anxiety; multimodal biosignals; emotion detection
MDPI and ACS Style

Lee, S.; Lee, T.; Yang, T.; Yoon, C.; Kim, S.-P. Detection of Drivers’ Anxiety Invoked by Driving Situations Using Multimodal Biosignals. Processes 2020, 8, 155.

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