Machine–Deep–Ensemble Learning Model for Classifying Cybersickness Caused by Virtual Reality Immersion

Bibliographic Data

ID22004564
AuthorsSeungjun Oh (0000-0002-5507-2445, Sangmyung University), Dong-Keun Kim (0000-0001-6093-126X, Sangmyung University, corresponding author)
Year2021
Volume24
Issue11
Pages729-736
Publication date2021-11-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueCyberpsychology Behavior and Social Networking (JOURNAL)
Journal identifiersISSN: 2152-2715 • E-ISSN: 2152-2723
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1089/cyber.2020.0613
PMID34375142
OpenAlexW3190129398
LanguageEN
Citations received1
References cited36

This study aims to classify cybersickness (CS) caused by virtual reality (VR) immersion through a machine-deep-ensemble learning model. The heart rate variability and respiratory signal parameters of 20 subjects were measured, while watching a VR video for ∼5 minutes. After the experiment, the subjects were examined for CS and questioned to determine their CS states. Based on the results, we constructed a machine-deep-ensemble learning model that could identify and classify VR immersion CS among subjects. The ensemble model comprised four stacked machine learning models (support vector machine [SVM], k-nearest neighbor [KNN], random forest, and AdaBoost), which were used to derive prediction data, and then, classified the prediction data using a convolution neural network. This model was a multiclass classification model, allowing us to classify subjects' CS into three states (neutral, non-CS, and CS). The accuracy of SVM, KNN, random forest, and AdaBoost was 94.23 percent, 92.44 percent, 93.20 percent, and 90.33 percent, respectively, and the ensemble model could classify the three states with an accuracy of 96.48 percent. This implied that the ensemble model has a higher classification performance than when each model is used individually. Our results confirm that CS caused by VR immersion can be detected as physiological signal data with high accuracy. Moreover, our proposed model can determine the presence or absence of CS as well as the neutral state. Clinical Trial Registration Number: 20-2021-1

AdaBoost · Deep learning · Ensemble forecasting · Ensemble learning · k-nearest neighbors algorithm · Machine learning · Random forest · Support vector machine · Computer Science · Heart Rate Variability and Autonomic Control · Mathematics · Virtual Reality Applications and Impacts · Artificial Intelligence

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