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Eye-based human-computer interaction (HCI) goes back at least to the early 1990s. Controlling a computer using the eyes traditionally meant extracting information from the gaze—that is, what a person was looking at. In an early work, Robert Jacob investigated gaze as an input modality for desktop computing.1 He discussed some of the human factors and technical aspects of performing common tasks such as pointing, moving screen objects, and menu selection. Since then, eye-based HCI has matured considerably. Today, eye tracking is used successfully as a measurement technique not only in the laboratory but also in commercial applications, such as marketing research and automotive usability studies.
in list: Eye Tracking Technology, HCI & Usability
ABSTRACT
With heavy competition between iPhone games, proper
playtesting is vital in making an easy to use, fun game. Eye
tracking can give valuable insights in player behavior but
current handheld eye tracking set-ups suffer technologial
limitations, inhibiting normal play. This study aims to
identify the merits and shortcomings of a new handheld
eyetracking set-up for qualitative user research. It is part of
a series of ongoing tests to improve the set-up. In this
study, seven participants played an iPhone puzzle game
using the new set-up. Results indicated the set-up was
suited for simple tasks like browsing, but interfered with
normal gaming too much for most players. Factors
contributing to interference were: Lack of depth perception,
unnatural handling, uncomfortable posture and enlarged
display of hands. Solutions for improvement are discussed:
With longer practice for players and with tweaks to the setup,
interference can be reduced or partly removed.
Accurate depth perception remains a challenge, however.
in list: HCI & Usability
ABSTRACT
Projector phones, handheld game consoles and many other
mobile devices increasingly include more than one display,
and therefore present a new breed of mobile Multi-Display
Environments (MDEs) to users. Existing studies illustrate
the effects of visual separation between displays in MDEs
and suggest interaction techniques that mitigate these
effects. Currently, mobile devices with heterogeneous
displays such as projector phones are often designed
without reference to visual separation issues; therefore it is
critical to establish whether concerns and opportunities
raised in the existing MDE literature apply to the emerging
category of Mobile MDEs (MMDEs). This paper
investigates the effects of visual separation in the context of
MMDEs and contrasts these with fixed MDE results, and
explores design factors for Mobile MDEs. Our study uses a
novel eye-tracking methodology for measuring switches in
visual context between displays and identifies that MMDEs
offer increased design flexibility over traditional MDEs in
terms of visual separation. We discuss these results and
identify several design implications.
in list: HCI & Usability
ABSTRACT
We investigate if the gaze (point of regard) can control a remote vehicle driving on a racing track. Five different input devices (on-screen buttons, mouse-pointing low-cost webcam eye tracker and two commercial eye tracking systems) provide heading and speed control on the scene view transmitted from the moving robot. Gaze control was found to be similar to mouse control. This suggests that robots and wheelchairs may be controlled "hands-free" through gaze. Low precision gaze tracking and image transmission delays had noticeable effect on performance.
in list: HCI & Usability , Eye Control
Executive Summary
With a worldwide increase in use of mobile devices, the interest for eye‐tracking the use of such has grown as well. Eye tracking can give information regarding a number of things, e.g. how well a graphical interface works, where on a physical device the user look for certain things such as buttons, and how the user’s attention shifts between the different parts of the phone when interacting with it. However, eye tracking small devices is not an uncomplicated task. Because of the way the human eye works, we can see a large part of the mobile device even if only one fixation has been registered by the eye tracker. Also the accuracy of the eye tracker (which is about 0.5° for all Tobii models) plays a more significant role the smaller the interface to be eye tracked becomes. In addition, as the mobile device is three dimensional, eye tracking interaction with it can result in data offsets and errors if not set up properly. In order to overcome these issues, several different setups have been developed. In our study, we tested three of these which we judged to be most practical and to provide the most robust data.
in list: Tobii White Papers
This paper presents StarGazer - a new 3D interface for gaze-based interaction and target selection using continuous pan and zoom. Through StarGazer we address the issues of interacting with graph structured data and applications (i.e. gaze typing systems) using low resolution eye trackers or small-size displays. We show that it is possible to make robust selection even with a large number of selectable items on the screen and noisy gaze trackers. A test with 48 subjects demonstrated that users who have never tried gaze interaction before could rapidly adapt to the navigation principles of StarGazer. We tested three different display sizes (down to PDAsized displays) and found that large screens are faster to navigate than small displays and that the error rate is higher for the smallest display. Half of the subjects were exposed to severe noise deliberately added on the cursor positions. We found that this had a negative impact on efficiency. However, the user remained in control and the noise did not seem to effect the error rate. Additionally, three subjects tested the effects of temporally adding noise to simulate latency in the gaze tracker. Even with a significant latency (about 200 ms) the subjects were able to type at acceptable rates. In a second test, seven subjects were allowed to adjust the zooming speed themselves. They achieved typing rates of more than eight words per minute without using language modeling. We conclude that the StarGazer application is an intuitive 3D interface for gaze navigation, allowing more selectable objects to be displayed on the screen than the accuracy of the gaze trackers would otherwise permit.
ACM Portal link:
http://portal.acm.org/citation.cfm?doid=1344471.1344521
in list: HCI & Usability , Eye Control
Abstract
Gaze tracking technology is a convenient interfacing method for mobile devices. Most previous studies used a large-sized desktop or head-mounted display. In this study, we propose a novel gaze tracking method using an active appearance model (AAM) and multiple support vector regression (SVR) on a mobile device. Our research has four main contributions. First, in calculating the gaze position, the amount of facial rotation and translation based on four feature values is computed using facial feature points detected by AAM. Second, the amount of eye rotation based on two feature values is computed for measuring eye gaze position. Third, to compensate for the fitting error of an AAM in facial rotation, we use the adaptive discrete Kalman filter (DKF), which applies a different velocity of state transition matrix to the facial feature points. Fourth, we obtain gaze position on a mobile device based on multiple SVR by separating the rotation and translation of face and eye rotation. Experimental results show that the root mean square (rms) gaze error is 36.94 pixels on the 4.5-in. screen of a mobile device with a screen resolution of 800×600 pixels.
in list: HCI & Usability , Eye Control
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