TruVR: Trustworthy Cybersickness Detection using Explainable Machine Learning
TruVR: Trustworthy Cybersickness Detection using Explainable Machine Learning
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DOI：
10.1109/ismar55827.2022.00096
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Ripan Kumar Kundu;Rifatul Islam;P. Calyam;K. A. Hoque
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作者：
Ripan Kumar Kundu;Rifatul Islam;P. Calyam;K. A. Hoque
在使用虚拟现实（VR）系统时，电脑病的特征可以是恶心、眩晕、头痛、眼睛疲劳和其他不适。之前报道的用于检测（分类）和预测（回归）VR晕电病的机器学习（ML）和深度学习（DL）算法使用黑箱模型;因此，它们缺乏可解释性。此外，VR传感器会生成大量数据，从而产生复杂而庞大的模型。因此，在晕电病检测模型中具有固有的可解释性可以显着提高模型的可信度，并深入了解ML/DL模型为什么以及如何做出特定决策。为了解决这个问题，我们提出了三种可解释的机器学习（xML）模型来检测和预测晕网病：1）可解释的提升机（EBM），2）决策树（DT）和3）逻辑回归（LR）。我们评估基于XML的模型与公开可用的生理和游戏数据集的cybersickness。结果表明，循证医学可以检测晕电病的准确率为99.75%和94.10%的生理和游戏数据集，分别。另一方面，在预测晕机时，EBM导致生理数据集的均方根误差（RMSE）为0.071，游戏数据集为0.27。此外，基于EBM的全局解释揭示了暴露长度、旋转和加速度是导致游戏数据集中的晕电的关键特征。相比之下，皮肤电反应和心率在生理数据集中最显著。我们的研究结果还表明，基于循证医学的本地解释可以确定个别样本的网络病的原因。我们相信所提出的基于XML的晕网病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晕网病检测和减少模型。
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 amved 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.