Key result
Deep ensemble model using physiological signals classifies VR-induced cybersickness with ~96% accuracy, outperforming individual models.
Why the study?
The study aimed to classify cybersickness caused by virtual reality immersion using a machine-deep-ensemble learning model based on physiological signals.
Does a machine-deep-ensemble learning model improve the classification accuracy of cybersickness caused by VR immersion compared to individual machine learning models?
Observational (n=20)
Does a machine-deep-ensemble learning model improve the classification accuracy of cybersickness caused by VR immersion compared to individual machine learning models?
A machine-deep-ensemble learning model using physiological signals can accurately classify cybersickness states caused by VR immersion.
Ensemble models may aid real-time VR cybersickness monitoring; leaves open validation in larger trials before clinical use.
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.
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Oh et al. (2021) conducted an observational in Cybersickness caused by virtual reality immersion (n=20). Machine-deep-ensemble learning model vs. Individual machine learning models (SVM, KNN, random forest, AdaBoost) was evaluated on Accuracy of classifying cybersickness into three states (neutral, non-CS, and CS). A machine-deep-ensemble learning model using physiological signals classified virtual reality-induced cybersickness into three states with 96.48% accuracy, outperforming individual models.
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