论文标题
TRUVR:可信赖的Cybersickness使用可解释的机器学习
TruVR: Trustworthy Cybersickness Detection using Explainable Machine Learning
论文作者
论文摘要
使用虚拟现实(VR)系统时,Cybersickness的特征是恶心,眩晕,头痛,眼睛疲劳和其他不适。先前报道的机器学习(ML)和深度学习(DL)算法用于检测(分类)和预测(回归)VR Cybersickness使用黑盒模型;因此,他们缺乏解释性。此外,VR传感器会产生大量数据,从而产生复杂和大型模型。因此,在Cybersickness检测模型中具有固有的解释性可以显着提高模型的可信度,并洞悉为什么ML/DL模型如何制定特定的决定。为了解决此问题,我们提出了三个可解释的机器学习(XML)模型来检测和预测Cybersickness:1)可解释的提升机(EBM),2)决策树(DT)和3)逻辑回归(LR)。我们通过公开可用的生理和游戏数据集评估了基于XML的模型。结果表明,EBM可以分别以99.75%和94.10%的精度检测Cybersickness,分别为生理和游戏数据集检测。另一方面,在预测Cybersickness的同时,EBM导致生理数据集的均方根误差(RMSE)为0.071,而游戏玩法数据集则导致0.27。此外,基于EBM的全球解释揭示了曝光的长度,旋转和加速度作为在游戏玩法数据集中引起Cybersickness的关键特征。相反,电流皮肤反应和心率在生理数据集中最为重要。我们的结果还表明,基于EBM的局部解释可以鉴定单个样本的引起网络智能因素。我们认为,提出的基于XML的Cybersickness检测方法可以帮助未来的研究人员理解,分析和设计更简单的Cybersickness检测和还原模型。
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.