Discrete-time signal processing
Discrete-time signal processing
Identifying fixations and saccades in eye-tracking protocols
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
“Saccade pickers” vs. “fixation pickers”: the effect of eye tracking instrumentation on research
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
Eyedraw: a system for drawing pictures with eye movements
Assets '04 Proceedings of the 6th international ACM SIGACCESS conference on Computers and accessibility
Improving the accuracy of gaze input for interaction
Proceedings of the 2008 symposium on Eye tracking research & applications
Improving eye cursor's stability for eye pointing tasks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Measuring gaze depth with an eye tracker during stereoscopic display
Proceedings of the ACM SIGGRAPH Symposium on Applied Perception in Graphics and Visualization
On the conspicuity of 3-D fiducial markers in 2-D projected environments
Proceedings of the Symposium on Eye Tracking Research and Applications
On the conspicuity of 3-D fiducial markers in 2-D projected environments
Proceedings of the Symposium on Eye Tracking Research and Applications
Visual attention to wayfinding aids in virtual environments
JVRC '13 Proceedings of the 5th Joint Virtual Reality Conference
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Previous work has shown significant differences in eye movement metrics recorded by devices differing in sampling rates. Two schools of thought have emerged on how to effectively compare such apparently disparate data. The first, termed here as upsampling, strives to process eye movement data recorded at a low sampling rate to allow comparison with data recorded at a high sampling rate, e. g., by fitting a cubic spline to the signal derivative (i.e., velocity). Instead, we suggest downsampling based on a two-pass solution in which data is first downsampled and smoothed prior to its velocity-based classification. Results indicate that given a similar experimental task, this approach gives more equitable results than other single-pass classification methods as they typically do not explicitly consider sampling rates.