Towards Improving Educational Virtual Reality by Classifying Distraction using Deep Learning

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dc.contributor.authorKhokhar, Adilen_US
dc.contributor.authorBorst, Christoph W.en_US
dc.contributor.editorHideaki Uchiyamaen_US
dc.contributor.editorJean-Marie Normanden_US
dc.date.accessioned2022-11-29T07:25:27Z
dc.date.available2022-11-29T07:25:27Z
dc.date.issued2022
dc.description.abstractDistractions can cause students to miss out on critical information in educational Virtual Reality (VR) environments. Our work uses generalized features (angular velocities, positional velocities, pupil diameter, and eye openness) extracted from VR headset sensor data (head-tracking, hand-tracking, and eye-tracking) to train a deep CNN-LSTM classifier to detect distractors in our educational VR environment. We present preliminary results demonstrating a 94.93% accuracy for our classifier, an improvement in both the accuracy and generality of features used over two recent approaches. We believe that our work can be used to improve educational VR by providing a more accurate and generalizable approach for distractor detection.en_US
dc.description.sectionheadersCognition
dc.description.seriesinformationICAT-EGVE 2022 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments
dc.identifier.doi10.2312/egve.20221279
dc.identifier.isbn978-3-03868-179-3
dc.identifier.issn1727-530X
dc.identifier.pages85-90
dc.identifier.pages6 pages
dc.identifier.urihttps://doi.org/10.2312/egve.20221279
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egve20221279
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies -> Machine learning; Human-centered computing -> Virtual reality
dc.subjectComputing methodologies
dc.subjectMachine learning
dc.subjectHuman centered computing
dc.subjectVirtual reality
dc.titleTowards Improving Educational Virtual Reality by Classifying Distraction using Deep Learningen_US
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