A plethora of recent research has proposed several automated methods based on machine learning (ML) and deep learning (DL) to detect cybersickness in Virtual reality (VR). However, these detection methods are perceived as computationally intensive and black-box methods. Thus, those techniques are neither trustworthy nor practical for deploying on standalone VR head-mounted displays (HMDs). This work presents an explainable artificial intelligence (XAI)-based framework VR-LENS for developing cybersickness detection ML models, explaining them, reducing their size, and deploying them in a Qualcomm Snapdragon 750G processor-based Samsung A52 device. Specifically, we first develop a novel super learning-based ensemble ML model for cybersickness detection. Next, we employ a post-hoc explanation method, such as SHapley Additive exPlanations (SHAP), Morris Sensitivity Analysis (MSA), Local Interpretable Model-Agnostic Explanations (LIME), and Partial Dependence Plot (PDP) to explain the expected results and identify the most dominant features. The super learner cybersickness model is then retrained using the identified dominant features. Our proposed method identified eye tracking, player position, and galvanic skin/heart rate response as the most dominant features for the integrated sensor, gameplay, and bio-physiological datasets. We also show that the proposed XAI-guided feature reduction significantly reduces the model training and inference time by 1.91X and 2.15X while maintaining baseline accuracy. For instance, using the integrated sensor dataset, our reduced super learner model outperforms the state-of-the-art works by classifying cybersickness into 4 classes (none, low, medium, and high) with an accuracy of 96% and regressing (FMS 1-10) with a Root Mean Square Error (RMSE) of 0.03.

翻译：近期大量研究提出了基于机器学习和深度学习的自动化方法，用于检测虚拟现实中的晕动症。然而，这些检测方法被视为计算密集型且不透明的黑盒方法，因此既缺乏可信度，也不适用于独立式虚拟现实头戴显示设备的实际部署。本文提出一种基于可解释人工智能的框架VR-LENS，用于开发晕动症检测的机器学习模型、解释模型行为、缩减模型规模，并最终部署于搭载高通骁龙750G处理器的三星A52设备上。具体而言，我们首先开发了一种新颖的基于超级学习的集成机器学习模型用于晕动症检测；其次，采用事后解释方法（包括SHAP、Morris敏感性分析、LIME和部分依赖图）解释预测结果并识别最显著特征；随后利用识别出的显著特征重新训练超级学习器晕动症模型。本方法确定眼动追踪、玩家位置以及皮肤电导/心率响应分别为集成传感器数据集、游戏过程数据集和生理数据集中的最显著特征。实验表明，所提出的XAI引导特征缩减方法在保持基准精度的同时，将模型训练和推理时间分别降低1.91倍和2.15倍。例如，在集成传感器数据集上，缩减后的超级学习器模型将晕动症分类为4个等级（无、低、中、高）的准确率达到96%，且回归预测的均方根误差仅为0.03。