Kalman filtering in the design of eye-gaze-guided computer interfaces

Authors:
Oleg V. Komogortsev;Javed I. Khan
Affiliations:
Perceptual Engineering Laboratory, Department of Computer Science, Kent State University, Kent, OH;Perceptual Engineering Laboratory, Department of Computer Science, Kent State University, Kent, OH
Venue:
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
Year:
2007

Citing 3
Cited 5

Eye tracking in advanced interface design

Virtual environments and advanced interface design
An evaluation of an eye tracker as a device for computer input2

CHI '87 Proceedings of the SIGCHI/GI Conference on Human Factors in Computing Systems and Graphics Interface
Eye Tracking Methodology: Theory and Practice

Eye Tracking Methodology: Theory and Practice

Eye movement prediction by Kalman filter with integrated linear horizontal oculomotor plant mechanical model

Proceedings of the 2008 symposium on Eye tracking research & applications
Input evaluation of an eye-gaze-guided interface: kalman filter vs. velocity threshold eye movement identification

Proceedings of the 1st ACM SIGCHI symposium on Engineering interactive computing systems
Instantaneous saccade driven eye gaze interaction

Proceedings of the International Conference on Advances in Computer Enterntainment Technology
Real time eye movement identification protocol

CHI '10 Extended Abstracts on Human Factors in Computing Systems
Comparison of eye movement filters used in HCI

Proceedings of the Symposium on Eye Tracking Research and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we design an Attention Focus Kalman Filter (AFKF) - a framework that offers interaction capabilities by constructing an eye-movement language, provides real-time perceptual compression through Human Visual System (HVS) modeling, and improves system's reliability. These goals are achieved by an AFKF through identification of basic eye-movement types in real-time, the prediction of a user's perceptual attention focus, and the use of the eye's visual sensitivity function and eye-position data signal de-noising.