Thinking Outside of the Box or Enjoying Your 2 Seconds of Frame?

  • Per BækgaardEmail author
  • Michael Kai Petersen
  • Jakob Eg Larsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9176)


The emergence of low cost eye tracking devices will make QS quantified self monitoring of eye movements attainable on next generation mobile devices, potentially allowing us to infer reactions related to fatigue or emotional responses on a continuous basis when interacting with the screens of smartphones and tablets. In the current study we explore whether consumer grade eye trackers, despite their reduced spatio-temporal resolution, are able to monitor fixations as well as frequencies of saccades and blinks that may characterize aspects of attention, and identify consistent individual patterns that may be modulated by our overall level of engagement.


Eye tracking Fixation Density Maps Fixation duration 



This work is supported in part by the Innovation Fund Denmark through the project Eye Tracking for Mobile Devices.


  1. 1.
    Bækgaard, P.: Simple python interface to the eye tribe eye tracker. (Accessed: 17 February 2015)
  2. 2.
    Carlei, C., Kerzel, D.: Gaze direction affects visuo-spatial short-term memory. Brain Cogn. 90, 63–68 (2014)CrossRefGoogle Scholar
  3. 3.
    Castelhano, M.S., Henderson, J.M.: Stable individual differences across images in human saccadic eye movements. Can. J. Exp. Psychol./Rev. Can. Psychol. Expérimentale 62(1), 1 (2008)CrossRefGoogle Scholar
  4. 4.
    Cazzoli, D., Antoniades, C.A., Kennard, C., Nyffeler, T., Bassetti, C.L., Müri, R.M.: Eye movements discriminate fatigue due to chronotypical factors and time spent on task-a double dissociation. PloS ONE 9(1), e87146 (2014)CrossRefGoogle Scholar
  5. 5.
    Corbetta, M., Shulman, G.L.: Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3(3), 201–215 (2002)CrossRefGoogle Scholar
  6. 6.
    Dalmaijer, E.: Is the low-cost eyetribe eye tracker any good for research?. Technical report. PeerJ PrePrints (2014)Google Scholar
  7. 7.
    Di Stasi, L.L., Catena, A., Canas, J.J., Macknik, S.L., Martinez-Conde, S.: Saccadic velocity as an arousal index in naturalistic tasks. Neurosci. Biobehav. Rev. 37(5), 968–975 (2013)CrossRefGoogle Scholar
  8. 8.
    Dodge, R.: The laws of relative fatigue. Psychol. Rev. 24(2), 89 (1917)CrossRefGoogle Scholar
  9. 9.
    Dodge, R., Cline, T.S.: The angle velocity of eye movements. Psychol. Rev. 8(2), 145 (1901)CrossRefGoogle Scholar
  10. 10.
    Kasprowski, P., Ober, J.: Eye movements in biometrics. In: Maltoni, D., Jain, A.K. (eds.) BioAW 2004. LNCS, vol. 3087, pp. 248–258. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  11. 11.
    Knickerbocker, H., Johnson, R.L., Altarriba, J.: Emotion effects during reading: influence of an emotion target word on eye movements and processing. Cogn. Emot. 29(5), 784–806 (2015). doi: 10.1080/02699931.2014.938023 CrossRefGoogle Scholar
  12. 12.
    Komogortsev, O., Holland, C., Karpov, A., Price, L.R.: Biometrics via oculomotor plant characteristics: Impact of parameters in oculomotor plant model. ACM Trans. Appl. Percept. (TAP) 11(4), 20 (2014)Google Scholar
  13. 13.
    Komogortsev, O.V., Jayarathna, S., Aragon, C.R., Mahmoud, M.: Biometric identification via an oculomotor plant mathematical model. In: Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications, pp. 57–60. ACM (2010)Google Scholar
  14. 14.
    Peirce, J.W.: Psychopy-psychophysics software in python. J. Neurosci. Methods 162(1), 8–13 (2007)CrossRefGoogle Scholar
  15. 15.
    Rigas, I., Komogortsev, O.V.: Biometric recognition via fixation density maps. In: International Society for Optics and Photonics. SPIE Defense+ Security, pp. 90750M–90750M (2014)Google Scholar
  16. 16.
    Schleicher, R., Galley, N., Briest, S., Galley, L.: Blinks and saccades as indicators of fatigue in sleepiness warnings: looking tired? Ergonomics 51(7), 982–1010 (2008)CrossRefGoogle Scholar
  17. 17.
    Sirevaag, E.J., Stern, J.A.: Ocular measures of fatigue and cognitive factors. In: Engineering psychophysiology: Issues and Applications, pp. 269–287 (2000)Google Scholar
  18. 18.
    TheEyeTribe: Api reference eyetribe-docs. (Accessed: 17 February 2015)
  19. 19.
    Wang, L., Stern, J.A.: Oculometric evaluation of subjects performing a vigilance task: The bakan continuous performance task. Unpublished Manuscript (1997)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Per Bækgaard
    • 1
    Email author
  • Michael Kai Petersen
    • 1
  • Jakob Eg Larsen
    • 1
  1. 1.Cognitive Systems Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKgs. LyngbyDenmark

Personalised recommendations