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.

中文翻译：

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用于对虚拟现实沉浸引起的网络病进行分类的机器-深度-集成学习模型

本研究旨在通过机器-深度-集成学习模型对由虚拟现实 (VR) 沉浸引起的网络病 (CS) 进行分类。在观看 VR 视频约 5 分钟的同时，测量了 20 名受试者的心率变异性和呼吸信号参数。实验结束后，受试者接受了 CS 的检查和询问以确定他们的 CS 状态。基于这些结果，我们构建了一个机器-深度-集成学习模型，可以识别和分类主体之间的 VR 沉浸式 CS。集成模型包括四个堆叠的机器学习模型（支持向量机 [SVM]、k-最近邻 [KNN]、随机森林和 AdaBoost），用于推导预测数据，然后使用卷积对预测数据进行分类神经网络。这个模型是一个多类分类模型，允许我们将受试者的 CS 分为三种状态（中性、非 CS 和 CS）。SVM、KNN、随机森林和AdaBoost的准确率分别为94.23%、92.44%、93.20%和90.33%，集成模型能够以96.48%的准确率对三种状态进行分类。这意味着集成模型比单独使用每个模型时具有更高的分类性能。我们的结果证实，可以将 VR 沉浸引起的 CS 检测为高精度的生理信号数据。此外，我们提出的模型可以确定 CS 的存在与否以及中性状态。集成模型可以对三种状态进行分类，准确率达到 96.48%。这意味着集成模型比单独使用每个模型时具有更高的分类性能。我们的结果证实，可以将 VR 沉浸引起的 CS 检测为高精度的生理信号数据。此外，我们提出的模型可以确定 CS 的存在与否以及中性状态。集成模型可以对三种状态进行分类，准确率达到 96.48%。这意味着集成模型比单独使用每个模型时具有更高的分类性能。我们的结果证实，可以将 VR 沉浸引起的 CS 检测为高精度的生理信号数据。此外，我们提出的模型可以确定 CS 的存在与否以及中性状态。