Inferring intent in eye-based interfaces

Abstract

While current eye-based interfaces offer enormous potential for efficient human-computer interaction, they also manifest the difficulty of inferring intent from user eye movements. This paper describes how fixation tracing facilitates the interpretation of eye movements and improves the flexibility and usability of eye-based interfaces. Fixation tracing uses hidden Markov models to map user actions to the sequential predictions of a cognitive process model. In a study of eye typing, results show that fixation tracing generates significantly more accurate interpretations than simpler methods and allows for more flexibility in designing usable interfaces. Implications for future research in eye-based interfaces and multimodal interfaces are discussed.