首页> 外文会议>Proceedings of the Human Factors and Ergonomics Society 2018 annual meeting >ReClass: An Application of Deep Learning for Real-time Reading Detection Using an Eye Tracking Device
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ReClass: An Application of Deep Learning for Real-time Reading Detection Using an Eye Tracking Device

机译:ReClass:深度学习在使用眼动仪的实时阅读检测中的应用

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We demonstrate the ReClass system, a real time reading detection classifier. ReClass detects if a user is reading text or not solely based on the user’s eye movements, without considering the content of the screen. We examined two machine learning approaches. In the first approach, we pre-processed the data using feature engineering and trained a Linear Support Vector Machine classifier. The second approach used a Deep Learning Neural Network; instead of feature engineering, we allowed the neural network to extract features from the raw data. The second approach outperformed the first by about 25%, achieving 96% accuracy. Our tool illustrates how Deep Learning can be a new innovative method for teaching machines to understand human behaviour. We discuss the potential applications of ReClass for educational assessment, medical diagnosis and training.
机译:我们演示了ReClass系统,这是一种实时读取检测分类器。 ReClass仅根据用户的眼动来检测用户是否正在阅读文本,而无需考虑屏幕内容。我们研究了两种机器学习方法。在第一种方法中,我们使用特征工程对数据进行预处理,并训练了线性支持向量机分类器。第二种方法是使用深度学习神经网络。代替特征工程,我们允许神经网络从原始数据中提取特征。第二种方法的性能比第一种方法高出约25%,达到了96%的准确度。我们的工具说明了深度学习如何成为教学机器理解人类行为的一种新颖的创新方法。我们讨论了ReClass在教育评估,医学诊断和培训方面的潜在应用。

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