This work explores a new direction in utilizing eye gaze for computer input. Gaze tracking has long been considered as an alternative or potentially superior pointing method for computer input. We believe that many fundamental limitations exist with traditional gaze pointing. In particular, it is unnatural to overload a perceptual channel such as vision with a motor control task. We therefore propose an alternative approach, dubbed MAGIC (Manual And Gaze Input Cascaded) pointing. With such an approach, pointing appears to the user to be a manual task, used for fine manipulation and selection. However, a large portion of the cursor movement is eliminated by warping the cursor to the eye gaze area, which encompasses the target. Two specific MAGIC pointing techniques, one conservative and one liberal, were designed, analyzed, and implemented with an eye tracker we developed. They were then tested in a pilot study. This earlystage exploration showed that the MAGIC pointing techniques might offer many advantages, including reduced physical effort and fatigue as compared to traditional manual pointing, greater accuracy and naturalness than traditional gaze pointing, and possibly faster speed than manual pointing. The pros and cons of the two techniques are discussed in light of both performance data and subjective reports.

In this paper we review the major approaches to Multimodal Human Computer Interaction, giving an overview of the field from a computer vision perspective. In particular, we focus on body, gesture, gaze, and affective interaction (facial expression recognition and emotion in audio). We discuss user and task modeling, and multimodal fusion, highlighting challenges, open issues, and emerging applications for Multimodal Human Computer Interaction (MMHCI) research.

As bigger screens and multi-monitor configurations become more popular, users employ higher mouse accelerations in order to traverse the screen reasonably quickly. The faster the mouse cursor moves, however, the more it seems to jump from one position to the next, as it is updated only at the refresh rate of the monitor. This lack of visual continuity increases the risk of users losing track of the cursor. The high-density cursor presented in this paper addresses this issue by filling in additional cursor images between actual cursor positions (temporal supersampling). Unlike existing techniques, such as the Windows mouse trail, the proposed technique preserves the responsiveness of the mouse cursor. We report the results of a user study in which the high-density cursor improved participants' performance on a Fitts' law task by up to 7% for target acquisitions across long distances. Moreover, the conditions of the high-density mouse that were tested were subtle enough that they were often not even detected, and were never considered distracting.

When acquiring a target located on a different screen, multi-monitor users face a challenge: differences in resolution and vertical and horizontal offsets between screens cause the mouse pointer to get warped, making the attempt to acquire the target difficult. Mouse ether eliminates warping effects by applying appropriate transformations to all mouse move events. In our user study, mouse ether improved participants' performance on a target acquisition task across two screens running at different resolutions by up to 28%. 7 of the 8 participants also strongly preferred using mouse ether to the control.

Much research has examined the use of awareness tools for enhancing coordination. However, the proposed tools tend to either make people aware of one information source in great detail or a variety of sources with very little detail. We refer to this problem as the detail-variety trade-off. Another problem with awareness tools is that they are not interchangeable. Specifically, users of these tools have only limited ability to route awareness information to different types of awareness tools that might better suit their work styles. We believe these two problems are primarily software engineering problems to which we present a solution called CASS (Cross Application Subscription Services), for obtaining awareness information from diverse sources and routing it to a variety of awareness tools. Keywords: Event and Notification Server Architectures, Awareness, Push Technologies 1

Text input with eye trackers can be implemented in many ways such as on-screen keyboards or context sensitive menu-selection techniques. We propose the use of off-screen targets and various schemes for decoding target hit sequences into text. Off-screen targets help to avoid the Midas' touch problem and conserve display area. However, the number and location of the off-screen targets is a major usability issue. We discuss the use of Morse code, our Minimal Device Independent Text Input Method (MDITIM), QuikWriting, and Cirrin-like target arrangements. Furthermore, we describe our experience with an experimental system that implements eye tracker controlled MDITIM for the Windows environment. Categories and Subject Descriptors: H.5.5 [Information Interfaces and Presentation]: User Interfaces - Input devices and Strategies, Interaction styles General Terms: Experimentation Additional Keywords: eye tracker, text input, off-screen targets, MDITIM, QuikWriting, Cirrin, Morse code. 1 INT...

We have developed a system for remedial reading instruction that uses visually controlled auditory prompting to help the user with recognition and pronunciation of words. Our underlying hypothesis is that the relatively unobtrusive assistance rendered by such a system will be more effective than previous computer aided approaches. We present a description of the design and implementation of our system and discuss a controlled study that we undertook to evaluate the usability of the Reading Assistant.

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To overcome the limitations of current technologies for remote collaboration, we propose a system that changes a video feed based on task properties, people’s actions, and message properties. First, we examined how participants manage different visual resources in a laboratory experiment using a collaborative task in which one partner (the helper) instructs another (the worker) how to assemble online puzzles. We analyzed helpers’ eye gaze as a function of the aforementioned parameters. Helpers gazed at the set of alternative pieces more frequently when it was harder for workers to differentiate these pieces, and less frequently over repeated trials. The results further suggest that a helper’s desired focus of attention can be predicted based on task properties, his/her partner’s actions, and message properties. We propose a conditional Markov model classifier to explore the feasibility of predicting gaze based on these properties. The accuracy of the model ranged from 65.40 % for puzzles with easyto-name pieces to 74.25 % for puzzles with more difficult to name pieces. The results suggest that we can use our model to automatically manipulate video feeds to show what helpers want to see when they want to see it. Categories and Subject Descriptors H5.3. Information interfaces and presentation (e.g., HCI): Group and organizational interfaces – collaborative computing, computer-supported collaborative work

With the development of novel interfaces controlled through multiple modalities, new approaches are needed to analyze the process of interaction with such interfaces and evaluate them at a fine grain of detail. In order to evaluate the usability and usefulness of such interfaces, one needs tools to collect and analyze richly detailed data pertaining to both the process and outcomes of user interaction. Eye tracking is a technology that can provide detailed data on the allocation and shifts of users ' visual attention across interface entities. Eye movement data, when combined with data from other input modalities (such as spoken commands, haptie actions with the keyboard and the mouse, etc.), results in just such a rich data set. However, integrating, analyzing and visualizing multimodal data on user interactions remains a difficult task. In this paper we report on a first step toward developing a suite of tools to facilitate this task. We designed and implemented an Eye Tracking Analysis System that generates combined gaze and action visualizations from eye movement data and interaction logs. This new visualization allows an experimenter to see the visual attention shills of users interleaved with their actions on each screen of a multi-screen interface. A pilot experiment on comparing two interfaces- a traditional interface and a speech-controlled one- to an educational multimedia application was carried out to test the utility of our tool.