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Improving the accuracy of gaze input for interaction
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Eye Tracking Research & Application archive
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
Authors
Manu Kumar  GazeWorks, Inc., Palo Alto, CA
Jeff Klingner  Stanford University, Stanford, CA
Rohan Puranik  Stanford University, Stanford, CA
Terry Winograd  Stanford University, Stanford, CA
Andreas Paepcke  Stanford University, Stanford, CA
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
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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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Monty, R. A., Senders, J. W., and Fisher, D. F. Eye Movements and the Higher Psychological Functions. 1978, Hillsdale, New Jersey, USA: Erlbaum.
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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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Collaborative Colleagues:
Manu Kumar: colleagues
Jeff Klingner: colleagues
Rohan Puranik: colleagues
Terry Winograd: colleagues
Andreas Paepcke: colleagues