论文标题
一种用于检测眼睛追踪数据混淆的神经架构
A Neural Architecture for Detecting Confusion in Eye-tracking Data
论文作者
论文摘要
在各种领域中深度学习的成功的鼓励下，我们研究了其方法对检测用户混淆在眼睛跟踪数据中的有效性的新应用。我们介绍了一种平行使用RNN和CNN子模型的体系结构，以利用我们数据的时间和视觉空间方面。用用户与ValueChart可视化工具的用户交互数据集进行的实验表明，我们的模型优于基于随机森林的现有模型，从而提高了22％的联合灵敏度和特异性。
Encouraged by the success of deep learning in a variety of domains, we investigate a novel application of its methods on the effectiveness of detecting user confusion in eye-tracking data. We introduce an architecture that uses RNN and CNN sub-models in parallel to take advantage of the temporal and visuospatial aspects of our data. Experiments with a dataset of user interactions with the ValueChart visualization tool show that our model outperforms an existing model based on Random Forests resulting in a 22% improvement in combined sensitivity & specificity.
