Vibrotactile feedback is widely used in mobile devices because it provides a discreet and private feedback channel. Gaze-based interaction, on the other hand, is useful in various applications due to its unique capability to convey the focus of interest. Gaze input is naturally available as people typically look at things they operate, but feedback from eye movements is primarily visual. Gaze interaction and the use of vibrotactile feedback have been two parallel fields of human–computer interaction research with a limited connection. Our aim was to build this connection by studying the temporal and spatial mechanisms of supporting gaze input with vibrotactile feedback. The results of a series of experiments showed that the temporal distance between a gaze event and vibrotactile feedback should be less than 250 ms to ensure that the input and output are perceived as connected. The effectiveness of vibrotactile feedback was largely independent of the spatial body location of vibrotactile actuators. In comparison to other modalities, vibrotactile feedback performed equally to auditory and visual feedback. Vibrotactile feedback can be especially beneficial when other modalities are unavailable or difficult to perceive. Based on the findings, we present design guidelines for supporting gaze interaction with vibrotactile feedback.

In gesture-based user interfaces, the effort needed for learning the gestures is a persistent problem that hinders their adoption in products. However, people's natural gaze paths form shapes during viewing. For example, reading creates a recognizable pattern. These gaze patterns can be utilized in human-technology interaction. We experimented with the idea of inducing specific gaze patterns by static drawings. The drawings included visual hints to guide the gaze. By looking at the parts of the drawing, the user's gaze composed a gaze gesture that activated a command. We organized a proof-of-concept trial to see how intuitive the idea is. Most participants understood the idea without specific instructions already on the first round of trials. We argue that with careful design the form of objects and especially their decorative details can serve as a gaze-based user interface in smart homes and other environments of ubiquitous computing.

Exploratory analysis of gaze data requires methods that make it possible to process large amounts of data while minimizing human labor. The conventional approach in exploring gaze data is to construct heatmap visualizations. While simple and intuitive, conventional heatmaps do not clearly indicate differences between groups of viewers or give estimates for the repeatability (i.e., which parts of the heatmap would look similar if the data were collected again). We discuss difference maps and significance maps that answer to these needs. In addition we describe methods based on automatic clustering that allow us to achieve similar results with cluster observation maps and transition maps. As demonstrated with our example data, these methods are effective in highlighting the strongest differences between groups more effectively than conventional heatmaps.

In a study with 12 participants we compared two smooth pursuit based widgets and one dwell time based widget in adjusting a continuous value. The circular smooth pursuit widget was found to be about equally efficient as the dwell based widget in our color matching task. The scroll bar shaped smooth pursuit widget exhibited lower performance and lower user ratings.

Modern interaction techniques like non-intrusive gestures provide means for interacting with distant displays and smart objects without touching them. We were interested in the effects of feedback modality (auditory, haptic or visual) and its combined effect with input modality on user performance and experience in such interactions. Therefore, we conducted two exploratory experiments where numbers were entered, either by gaze or hand, using gestures composed of four stroke elements (up, down, left and right). In Experiment 1, a simple feedback was given on each stroke during the motor action of gesturing: an audible click, a haptic tap or a visual flash. In Experiment 2, a semantic feedback was given on the final gesture: the executed number was spoken, coded by haptic taps or shown as text. With simultaneous simple feedback in Experiment 1, performance with hand input was slower but more accurate than with gaze input. With semantic feedback in Experiment 2, however, hand input was only slower. Effects of feedback modality were of minor importance; nevertheless, semantic haptic feedback in Experiment 2 showed to be useless at least without extensive training. Error patterns differed between both input modes, but again not dependent on feedback modality. Taken together, the results show that in designing gestural systems, choosing a feedback modality can be given a low priority; it can be chosen according to the task, context and user preferences.

Anticipating the emergence of gaze tracking capable mobile devices, we are investigating the use of gaze as an input modality in handheld mobile devices. We conducted a study of combining gaze gestures with vibrotactile feedback. Gaze gestures were used as an input method in a mobile device and vibrotactile feedback as a new alternative way to give confirmation of interaction events. Our results show that vibrotactile feedback significantly improved the use of gaze gestures. The tasks were completed faster and rated easier and more comfortable when vibrotactile feedback was provided.

Eye tracking input often relies on visual and auditory feedback. Haptic feedback offers a previously unused alternative to these established methods. We describe a study to determine the natural time limits for haptic feedback to gazing events. The target is to determine how much time we can use to evaluate the user gazed object and decide if we are going to give the user a haptic notification on that object or not. The results indicate that it is best to get feedback faster than in 250 milliseconds from the start of fixation of an object. Longer delay leads to increase in incorrect associations between objects and the feedback. Delays longer than 500 milliseconds were confusing for the user.

Compared to the mouse, eye pointing is inaccurate. As a consequence, small objects are difficult to point by gaze alone. We suggest using a combination of eye pointing and subtle head movements to achieve accurate hands-free pointing in a conventional desktop computing environment. For tracking the head movements, we exploited information of the eye position in the eye tracker's camera view. We conducted a series of three experiments to study the potential caveats and benefits of using head movements to adjust gaze cursor position. Results showed that head-assisted eye pointing significantly improves the pointing accuracy without a negative impact on the pointing time. In some cases participants were able to point almost 3 times closer to the target's center, compared to the eye pointing alone (7 vs. 19 pixels). We conclude that head assisted eye pointing is a comfortable and potentially very efficient alternative for other assisting methods in the eye pointing, such as zooming.

Early work on applied eye tracking investigated gaze as an input modality to interact with a desktop computer and discussed some of the human factors and technical aspects involved in performing common computer tasks with the eyes such as pointing and menu selection. Since then, eye tracking technology has considerably matured. Research on eye-based interaction is starting to gain interest in various specialized areas that are no longer restricted to desktop environment, such as virtual reality, human-human and humanrobot interaction. There is also a growing interest to take eye tracking out into the wild, to mobile and pervasive settings.

We propose a combination of gaze pointing and head gestures for enhanced hands-free interaction. Instead of the traditional dwell-time selection method, we experimented with five simple head gestures: nodding, turning left/right, and tilting left/right. The gestures were detected from the eye-tracking data by a range-based algorithm, which was found accurate enough in recognizing nodding and left-directed gestures. The gaze estimation accuracy did not noticeably suffer from the quick head motions. Participants pointed to nodding as the best gesture for occasional selections tasks and rated the other gestures as promising methods for navigation (turning) and functional mode switching (tilting). In general, dwell time works well for repeated tasks such as eye typing. However, considering multimodal games or transient interactions in pervasive and mobile environments, we believe a combination of gaze and head interaction could potentially provide a natural and more accurate interaction method.

Recent developments in mobile eye tracking equipment and automated eye movement analysis point the way toward unobtrusive eye-based human-computer interfaces that are pervasively usable in everyday life. We call this new paradigm pervasive eye tracking - continuous eye monitoring and analysis 24/7. PETMEI 2011 provides a forum for researcher from human-computer interaction, context-aware computing, and eye tracking to discuss techniques and applications that go beyond classical eye tracking and stationary eye-based interaction. We aim to discuss the implications of pervasive eye tracking for context-aware computing and to identify the key research challenges of mobile eye-based interaction. The long-term goal is to create a strong interdisciplinary research community linking these research fields together and to establish the workshop as the premier forum for research on pervasive eye tracking and mobile eye-based interaction.

Previous research shows that text entry by gaze using dwell time is slow, about 5-10 words per minute (wpm). These results are based on experiments with novices using a constant dwell time, typically between 450 and 1000 ms. We conducted a longitudinal study to find out how fast novices learn to type by gaze using an adjustable dwell time. Our results show that the text entry rate increased from 6.9 wpm in the first session to 19.9 wpm in the tenth session. Correspondingly, the dwell time decreased from an average of 876 ms to 282 ms, and the error rates decreased from 1.28% to .36%. The achieved typing speed of nearly 20 wpm is comparable with the result of 17.3 wpm achieved in an earlier, similar study with Dasher.

Dasher is one of the best known inventions in the area of text entry in recent years. It can be used with many input devices, but studies on user performance with it are still scarce. We ran a longitudinal study where 12 participants transcribed Finnish text with Dasher in ten 15-minute sessions using a Tobii 1750 eye tracker as a pointing device. The mean text entry rate was 2.5 wpm during the first session and 17.3 wpm during the tenth session. Our results show that very high text entry rates can be achieved with eye-operated Dasher, but only after several hours of training.

The tutorial provides examples, experiences and design guidelines for using eye-gaze in human-computer interaction. The goal of the tutorial is to give insight into exploiting the information about gaze direction in human-computer interaction. The participants will learn the basics of eye-tracking, but the focus of the tutorial is on the interaction issues. After the tutorial, the participants understand the pros and cons of using gaze for real-time input. The tutorial consists of lectures and live demonstrations with a state-of-the-art eye-tracking device.

Eye typing provides means of communication especially for people with severe disabilities. Recent research indicates that the type of feedback impacts typing speed, error rate, and the user's need to switch her gaze between the on-screen keyboard and the typed text field. The current study focuses on the issues of feedback when a short dwell time (450 ms vs. 900 ms in a previous study) is used. Results show that the findings obtained using longer dwell times only partly apply for shorter dwell times. For example, with a short dwell time, spoken feedback results in slower text entry speed and double entry errors. A short dwell time requires sharp and clear feedback that supports the typing rhythm.

Eye typing provides a means of communication for severely handicapped people, even those who are only capable of moving their eyes. This paper considers the features, functionality and methods used in the eye typing systems developed in the last twenty years. Primary concerned with text production, the paper also addresses other communication related issues, among them customization and voice output.

Eye-aware applications have existed for long, but mostly for very special and restricted target populations. We have designed and are currently implementing an eye-aware application, called iDict, which is a general-purpose translation aid aimed at mass markets. iDict monitors the user's gaze path while s/he is reading text written in a foreign language. When the reader encounters difficulties, iDict steps in and provides assistance with the translation. To accomplish this, the system makes use of information obtained from reading research, a language model, and the user profile. This paper describes the idea of the iDict application, the design problems and the key solutions for resolving these problems.