| Improving the accuracy of gaze input for interaction |
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Eye Tracking Research & Application
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Proceedings of the 2008 symposium on Eye tracking research & applications
table of contents
Savannah, Georgia
POSTER SESSION: Late breaking results: poster presentations
table of contents
Pages 65-68
Year of Publication: 2008
ISBN:978-1-59593-982-1
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Authors
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Manu Kumar
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GazeWorks, Inc., Palo Alto, CA
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Jeff Klingner
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Stanford University, Stanford, CA
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Rohan Puranik
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Stanford University, Stanford, CA
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Terry Winograd
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Stanford University, Stanford, CA
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Andreas Paepcke
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Stanford University, Stanford, CA
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| Bibliometrics |
Downloads (6 Weeks): 13, Downloads (12 Months): 127, Citation Count: 2
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ABSTRACT
Using gaze information as a form of input poses challenges based on the nature of eye movements and how we humans use our eyes in conjunction with other motor actions. In this paper, we present three techniques for improving the use of gaze as a form of input. We first present a saccade detection and smoothing algorithm that works on real-time streaming gaze information. We then present a study which explores some of the timing issues of using gaze in conjunction with a trigger (key press or other motor action) and propose a solution for resolving these issues. Finally, we present the concept of Focus Points, which makes it easier for users to focus their gaze when using gaze-based interaction techniques. Though these techniques were developed for improving the performance of gaze-based pointing, their use is applicable in general to using gaze as a practical form of input.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T. 2002. A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing. 50(2): p. 174.
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Kumar, M. 2007. GUIDe Saccade Detection and Smoothing Algorithm. Technical Report CSTR 2007-03, Stanford University
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Kumar, M., Garfinkel, T., Boneh, D., and Winograd, T. 2007a. Reducing Shoulder-surfing by Using Gaze-based Password Entry. Technical Report CSTR 2007-05, Stanford University
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[doi> 10.1145/765891.765979]
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Tobii Technology, AB. 2006 Tobii 1750 Eye Tracker. http://www.tobii.com.
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Yarbus, A. L. Eye Movements and Vision. 1967, New York: Plenum Press.
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INDEX TERMS
Primary Classification:
H.
Information Systems
H.5
INFORMATION INTERFACES AND PRESENTATION (I.7)
H.5.2
User Interfaces (D.2.2, H.1.2, I.3.6)
Subjects:
Input devices and strategies (e.g., mouse, touchscreen)
General Terms:
Algorithms,
Design,
Human Factors,
Performance
Keywords:
eye tracking,
eye-hand coordination,
fixation smoothing,
focus points,
gaze input,
gaze-enhanced user interface design
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