Users hold their mobile phones at varying distances depending on their posture, the application being used, and the task’s nature. Without considering such variation when designing UI target sizes limits the applicability of gaze selection for everyday interaction with mobile devices. Towards this end, we conducted a user study (N = 24) to investigate the implications of different target sizes and viewing across different screen regions. While larger targets generally improve accuracy and decrease precision, accuracy is significantly higher in the horizontal than in the vertical direction. This subsequently led us to find that increasing the tracking area in the vertical direction only, while maintaining the same visual target size, significantly improves accuracy. This suggests that visually smaller targets with larger vertical tracking areas enhance accuracy. Based on our results, we present concrete design guidelines for developers to optimise target sizes on gaze-enabled mobile devices to improve accuracy across varying user-to-screen distances.

Gaze is promising for hands-free interaction on mobile devices. However, it is not clear how gaze interaction methods compare to each other in mobile settings. This paper presents the first experiment in a mobile setting that compares three of the most commonly used gaze interaction methods: Dwell time, Pursuits, and Gaze gestures. In our study, 24 participants selected one of 2, 4, 9, 12 and 32 targets via gaze while sitting and while walking. Results show that input using Pursuits is faster than Dwell time and Gaze gestures especially when there are many targets. Users prefer Pursuits when stationary, but prefer Dwell time when walking. While selection using Gaze gestures is more demanding and slower when there are many targets, it is suitable for contexts where accuracy is more important than speed. We conclude with guidelines for the design of gaze interaction on handheld mobile devices.

With the advancement of smartphone technology, it is now possible for smartphones to run eye-tracking using the front-facing camera, enabling hands-free interaction by empowering mobile users with novel gaze-based input techniques. While several gaze-based interaction techniques have been proposed in the literature, these techniques were deployed in settings different from daily gaze interaction with mobile devices, posing several unique challenges. The user’s holding posture may hinder the camera’s view of their face during the interaction, the front-facing camera may be obstructed by the user’s clothing or hands, or the environment is shaky due to the user’s movements and the dynamic environment. This PhD research investigates the usability of state-of-the-art gaze-based input techniques in mobile settings, develops a novel concept of combining multiple gaze-based techniques, and addresses the challenges imposed by the unique aspects of these devices.

Gaze is promising for natural and spontaneous interaction with public displays, but current gaze-enabled displays require movement-hindering stationary eye trackers or cumbersome head-mounted eye trackers. We propose and evaluate GazeCast – a novel system that leverages users’ handheld mobile devices to allow gaze-based interaction with surrounding displays. In a user study (N = 20), we compared GazeCast to a standard webcam for gaze-based interaction using Pursuits. We found that while selection using GazeCast requires more time and physical demand, participants value GazeCast’s high accuracy and flexible positioning. We conclude by discussing how mobile computing can facilitate the adoption of gaze interaction with pervasive displays.