The comprehension of the correlation between hand and gaze is important in HCI as it provides foundations to build rich and natural user experiences on devices. Despite the interest in describing this correlation in Internet-related activities, notably search activities, the studies that have been done so far only focus on the mouse as the proxy for the hand. Thanks to the touchscreen, tablets allow us to investigate the hand-eye correlation with direct touch input instead. We designed a gaze and touch data collection via a user study involving Internet-based tasks on a tablet in order to explore the data, detail the correlation between touch input and gaze, and in a further step, use these results to suggest a gaze prediction model based on touch and context.

Our work investigates the use of gaze and multitouch to fluidly perform rotate-scale-translate (RST) tasks on large displays. The work specifically aims to understand if gaze can provide benefit in such a task, how task complexity affects performance, and how gaze and multitouch can be combined to create an integral input structure suited to the task of RST. We present four techniques that individually strike a different balance between gaze-based and touch-based translation while maintaining concurrent rotation and scaling operations. A 16 participant empirical evaluation revealed that three of our four techniques present viable options for this scenario, and that larger distances and rotation/scaling operations can significantly affect a gaze-based translation configuration. Furthermore we uncover new insights regarding multimodal integrality, finding that gaze and touch can be combined into configurations that pertain to integral or separable input structures.

Within a pervasive computing environment, we see content on shared displays that we wish to acquire and use in a specific way i.e., with an application on a personal device, transferring from point-to-point. The eyes as input can indicate intention to interact with a service, providing implicit pointing as a result. In this paper we investigate the use of gaze and manual input for the positioning of gaze-acquired content on personal devices. We evaluate two main techniques, (1) Gaze Positioning, transfer of content using gaze with manual input to confirm actions, (2) Manual Positioning, content is selected with gaze but final positioning is performed by manual input, involving a switch of modalities from gaze to manual input. A first user study compares these techniques applied to direct and indirect manual input configurations, a tablet with touch input and a laptop with mouse input. A second study evaluated our techniques in an application scenario involving distractor targets. Our overall results showed general acceptance and understanding of all conditions, although there were clear individual user preferences dependent on familiarity and preference toward gaze, touch, or mouse input.

The shared displays in our environment contain content that we desire. Furthermore, we often acquire content for a specific purpose, i.e., the acquisition of a phone number to place a call. We have developed a content transfer concept, Eye Drop. Eye Drop provides techniques that allow fluid content acquisition, transfer from shared displays, and local positioning on personal devices using gaze combined with manual input. The eyes naturally focus on content we desire. Our techniques use gaze to point remotely, removing the need for explicit pointing on the user's part. A manual trigger from a personal device confirms selection. Transfer is performed using gaze or manual input to smoothly transition content to a specific location on a personal device. This work demonstrates how techniques can be applied to acquire and apply actions to content through a natural sequence of interaction. We demonstrate a proof of concept prototype through five implemented application scenarios.

Eye gaze is a compelling interaction modality but requires user calibration before interaction can commence. State of the art procedures require the user to fixate on a succession of calibration markers, a task that is often experienced as difficult and tedious. We present pursuit calibration, a novel approach that, unlike existing methods, is able to detect the user's attention to a calibration target. This is achieved by using moving targets, and correlation of eye movement and target trajectory, implicitly exploiting smooth pursuit eye movement. Data for calibration is then only sampled when the user is attending to the target. Because of its ability to detect user attention, pursuit calibration can be performed implicitly, which enables more flexible designs of the calibration task. We demonstrate this in application examples and user studies, and show that pursuit calibration is tolerant to interruption, can blend naturally with applications and is able to calibrate users without their awareness.

Pervasive eye-based interaction refers to the vision of eye-based interaction becoming ubiquitously usable in everyday life, e. g. across multiple displays in the environment. While current head-mounted eye trackers work well for interaction with displays at similar distances, the scene camera often fails to cover both remote and close proximity displays, e. g. a public display on a wall and a handheld portable device. In this paper we describe an approach that allows for robust detection and gaze mapping across multiple such displays. Our approach uses an additional scene camera to extend the viewing and gaze mapping area of the eye tracker and automatically switches between both cameras depending on the display in view. Results from a pilot study show that our system achieves a similar gaze estimation accuracy to a single-camera system while at the same time increasing usability.