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Event detection in eye-tracking data with static and dynamic stimuli

Event detection in eye-tracking data with static and dynamic stimuli

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Event detection in eye-tracking data with static and dynamic stimuli

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  1. Event detection in eye-tracking data with static and dynamic stimuli Linnéa Larsson1,2, Martin Stridh1 & Marcus Nyström2 1Dept. of Electrical and Information Technology, Lund University, Lund, Sweden 2Humanities Laboratory, Lund University, Lund, Sweden

  2. Background • Morecommon to use video as stimuli in eye-tracking studies Main problems: • Most of the algorithmsdeveloped for static stimuli do not work for dynamic stimuli • Smoothpursuitrender the detection of other events difficult

  3. Objective • To develop an algorithm for detection of the foureyemovements; fixations, saccades, smoothpursuits and glissades. And it has been of special importance that it can be used for bothstatic and dynamic stimuli. • To develop a tool and a method for performance evaluation of an algorithm. x- and y- coordinates x- and y- coordinates time time

  4. Methods • Data collection • Algorithmdevelopment • Evaluation of the performance of the algorithm

  5. Data collection • Data wasrecordedbinocularly with SMI tower-mountedeye-tracker, with sampling freuquency 500 Hz • 33 participants (21 males/12 females), average age 31.2 (±9.9) years • Data wasrecorded with: • Static stimuli • Images (5 trials) • Texts (5 trials) • Dynamic stimuli • Short videos (6 trials) • Moving points (38 trials)

  6. Static stimuli Image Text

  7. Dynamic stimuli Video Moving point

  8. Algorithmdevelopment Structure and principle of the algorithm Input Output Saccade detection Glissadedetection Smoothpursuitdetection Fixationdetection • Preprocessing

  9. Algorithmdevelopment Structure and principle of the algorithm Input Output Saccade detection Glissadedetection Smoothpursuitdetection Fixationdetection • Preprocessing • Input signal • x- and y- coordinates • size of the pupil • timestamp

  10. Algorithmdevelopment Structure and principle of the algorithm Input Output Saccade detection Glissadedetection Smoothpursuitdetection Fixationdetection • Preprocessing • Preprocessing • Marked and excluded from furtheranalysis: • Blinks • Coordinatesrecordedoutside the screen • Velocity > 1000°/s and acceleration > 100 000°/s2

  11. Algorithmdevelopment Structure and principle of the algorithm Input Output Saccade detection Glissadedetection Smoothpursuitdetection Fixationdetection • Preprocessing • Saccadedetection • Based on acceleration • Uniform direction • Noiselevel

  12. Algorithmdevelopment Structure and principle of the algorithm Input Output Saccade detection Glissadedetection Smoothpursuitdetection Fixationdetection • Preprocessing • Glissadedetection • Velocity • Noiselevel

  13. Algorithmdevelopment Structure and principle of the algorithm Input Output Saccade detection Glissadedetection Smoothpursuitdetection Fixationdetection • Preprocessing • Smoothpursuitdetection • Compute the directionblockwise • Test the uniformity of the direction

  14. Algorithmdevelopment Structure and principle of the algorithm Input Output Saccade detection Glissadedetection Smoothpursuitdetection Fixationdetection • Preprocessing • Fixationdetection • Dispersion is calculatedblockwise

  15. Algorithm development Structure and principle of the algorithm Output Input Saccade detection Glissadedetection Smoothpursuitdetection Fixationdetection • Preprocessing • Output signal • Labelled data samples

  16. Evaluation of the algorithm • Events is manuallydetected by an expert in parts of the dataset. • To perform the manual detection a Matlab GUI is developed. • The results of the manual detection is compared to the event detection of the algorithm.

  17. Matlab GUI for manual event detection

  18. Matlab GUI for manual event detection

  19. Results Fixation Saccade Glissade Smoothpursuit

  20. Results Fixation Saccade Glissade Smoothpursuit

  21. Results – saccadedetection Images Moving points

  22. Results – glissadedetection Images Moving points

  23. Results – smoothpursuitdetection Images Moving points

  24. Results – fixationdetection Images Moving points

  25. Discussion and conclusions • An algorithm has beendeveloped that candetect the foureyemovements; fixations, saccades, smoothpursuit and glissades, independent of stimuli. • Saccadedetectionworkswell, even in presence of smoothpursuit. • A tool and methodology for algorithmevaluation has beendeveloped. • With a morecleartechnical definition of a glissade the performance of the detectioncan be improved. • Using data from botheyesmayimprove the performance of the smoothpursuitdetection.