Reanalyzing Effective Eye-related Information for Developing User's Intent Detection Systems

Studies on gaze-based interactions have utilized natural eye-related information to detect user intent. Most use a machine learningbased approach to minimize the cost of choosing appropriate eyerelated information. While those studies demonstrated the effectiveness of an intent detection system, understanding which eye-related information is useful for interactions is important. In this paper, we reanalyze how eye-related information affected the detection performance of a previous study to develop better intent detection systems in the future. Specifically, we analyzed two aspects of dimensionality reduction and adaptation to different tasks. The results showed that saccade and fixation are not always useful, and the direction of gaze movement could potentially cause overfitting.