Eye gaze is considered a promising interaction modality in extende reality (XR) environments. However, determining selection intention from gaze data often requires additional manual selection techniques. We present a Bayesian-based machine learning (ML) model to predict user selection intention in real-time using only gaze data. Our model uses a Bayesian approach to transform gaze data into selection probabilities, which are then fed into an ML model to discriminate selection intentions. In Study 1, our model achieved real-time inference with an accuracy of 0.97 and an F1 score of 0.96. In Study 2, we found that the selection intention inferred by our model enables more comfortable and accurate interactions compared to traditional techniques.

翻译：在扩展现实（XR）环境中，视线被视为一种极具潜力的交互模态。然而，从视线数据中判定选择意图通常需要额外的手动选择技术。本文提出了一种基于贝叶斯的机器学习（ML）模型，该模型仅使用视线数据即可实时预测用户的选择意图。我们的模型采用贝叶斯方法将视线数据转换为选择概率，随后将其输入至机器学习模型以判别选择意图。在研究1中，我们的模型实现了实时推理，准确率达到0.97，F1分数为0.96。在研究2中，我们发现相较于传统技术，通过本模型推断出的选择意图能够实现更舒适且更精确的交互。