Cybersickness can be characterized by nausea, vertigo, headache, eye strain, and other discomforts when using virtual reality (VR) systems. The previously reported machine learning (ML) and deep learning (DL) algorithms for detecting (classification) and predicting (regression) VR cybersickness use black-box models; thus, they lack explainability. Moreover, VR sensors generate a massive amount of data, resulting in complex and large models. Therefore, having inherent explainability in cybersickness detection models can significantly improve the model's trustworthiness and provide insight into why and how the ML/DL model arrived at a specific decision. To address this issue, we present three explainable machine learning (xML) models to detect and predict cybersickness: 1) explainable boosting machine (EBM), 2) decision tree (DT), and 3) logistic regression (LR). We evaluate xML-based models with publicly available physiological and gameplay datasets for cybersickness. The results show that the EBM can detect cybersickness with an accuracy of 99.75% and 94.10% for the physiological and gameplay datasets, respectively. On the other hand, while predicting the cybersickness, EBM resulted in a Root Mean Square Error (RMSE) of 0.071 for the physiological dataset and 0.27 for the gameplay dataset. Furthermore, the EBM-based global explanation reveals exposure length, rotation, and acceleration as key features causing cybersickness in the gameplay dataset. In contrast, galvanic skin responses and heart rate are most significant in the physiological dataset. Our results also suggest that EBM-based local explanation can identify cybersickness-causing factors for individual samples. We believe the proposed xML-based cybersickness detection method can help future researchers understand, analyze, and design simpler cybersickness detection and reduction models.
翻译:在使用虚拟现实(VR)系统时,网络偏好可以表现为恶心、眩晕、头痛、眼力紧张和其他不适。先前报告的机器学习(ML)和深学习(DL)算法,用于检测(分类)和预测(回归)VR网络偏好,使用黑盒模型;因此,它们缺乏解释性。此外,VR传感器产生大量数据,导致复杂和大型模型。因此,在网络偏差检测模型中固有的解释性解释性可以大大改善模型的可信度,并使人们了解ML/DL网络偏差模型为何和如何在具体决定中达到的。为了解决这个问题,我们提出了三种可以解释的机器学习(xML)算法,用于检测(缩略)和预测(回)VR) VR网络偏差的算法,因此它们缺乏解释性。我们用基于公开的生理和游戏偏差的模型来评估基于XMLML的模型,可以识别网络偏差。结果还显示EBM公司未来能检测到网络偏差,准确性为99-75%和94.0的网络偏差的网络偏差,而导致 Erick Rick Rick 数据流数据流数据流数据(分别显示的深度数据流数据,同时计算结果,而导致BMLMLMRBR的更精确数据,而导致的更精确性数据。