Towards Improving Educational Virtual Reality by Classifying Distraction using Deep Learning
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Date
2022
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Distractions 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.
Description
CCS Concepts: Computing methodologies -> Machine learning; Human-centered computing -> Virtual reality
@inproceedings{10.2312:egve.20221279,
booktitle = {ICAT-EGVE 2022 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments},
editor = {Hideaki Uchiyama and Jean-Marie Normand},
title = {{Towards Improving Educational Virtual Reality by Classifying Distraction using Deep Learning}},
author = {Khokhar, Adil and Borst, Christoph W.},
year = {2022},
publisher = {The Eurographics Association},
ISSN = {1727-530X},
ISBN = {978-3-03868-179-3},
DOI = {10.2312/egve.20221279}
}