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      Comparing Pupil Dilation, Head Movement, and EEG for Distraction Detection of Drivers

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      Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)

      Human Computer Interaction Conference

      4 - 6 July 2018

      Cognitive load, distraction, drivers, automotive, pupil dilation, head yaw, EEG, FFT, DWT, CWT

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          This paper investigates the use of pupil dilation, head movement and EEG for detecting distraction and cognitive load of drivers while performing secondary tasks in an automotive environment. We tracked pupil dilation from Tobii Pro Glasses 2, head movement from Kinect and EEG from Emotive Insight system. We have analyzed data using Fast Fourier Transform, Continuous Wavelet Transform, and Discrete Wavelet Transform for the full-length signal as well as in windows of 1 second for real-time implementation. We investigated detection of distraction and cognitive load from three different conditions - free driving, driving with lane change, driving with lane change and operating secondary task for each participant in a driving simulator. Our results show that the pupil dilation, head yaw, and EEG can detect the increase in cognitive load due to operation of secondary task within a time buffer of 1 second which can be adapted for real-time implementation. We have also found that FFT of Pupil dilation shows significant categorization of normal and distracted states than the categorization by DWT which contrasts with state of the art methods. Finally, we have proposed an expert system to alert drivers utilizing the signal processing analysis.

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          Most cited references 20

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          Identifying cognitive state from eye metrics.

          This paper describes a new approach for identifying cognitive state by using information obtained only from the eye. Data are collected from cameras mounted on a lightweight headband. A set of eye metrics captures essential eye information from the raw data of pupil size and point-of-gaze. The metrics are easily calculated every second, so that the entire set of metrics can be computed in real time. Three studies provide empirical evidence to test whether the eye metrics are sufficient to discriminate between two different cognitive states. The first study examines the states of relaxed and engaged in the context of problem solving. The second study looks at the states of focused and distracted attention in the context of driving. The third study inspects the states of alert and fatigued in the context of visual search. Two statistical models are used to classify cognitive state for all three studies: linear discriminant function analysis and non-linear neural network analysis. Data for the models are eye metrics computed at 1-, 4-, and 10-s intervals. All discriminant function analyses are statistically significant, and classification rates are high. Neural network models have equal or better performance than discriminant function models across all three studies. The seven eye metrics successfully discriminate between the states in all studies. Models from individual participants as well as the aggregate model over all participants are successful in identifying cognitive states based on task condition. Classification rates compare favorably with similar studies.
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            Estimating cognitive load using remote eye tracking in a driving simulator

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              The Index of Cognitive Activity: measuring cognitive workload

               S.P. Marshall (2002)

                Author and article information

                July 2018
                July 2018
                : 1-5
                Centre for Product Design and Manufacturing

                Indian Institute of Science, Bangalore, India
                © Prabhakar et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit

                Proceedings of the 32nd International BCS Human Computer Interaction Conference
                Belfast, UK
                4 - 6 July 2018
                Electronic Workshops in Computing (eWiC)
                Human Computer Interaction Conference
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page):
                Electronic Workshops in Computing


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