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Life in the Fast Lane: Effect of Language and Calibration Accuracy on the Speed of Text Entry by Gaze

  • Kari-Jouko RäihäEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9296)

Abstract

Numerous techniques have been developed for text entry by gaze, and similarly, a number of evaluations have been carried out to determine the efficiency of the solutions. However, the results of the published experiments are inconclusive, and it is unclear what causes the difference in their findings. Here we look particularly at the effect of the language used in the experiment. A study where participants entered text both in English and in Finnish does not show an effect of language structure: the entry rates were reasonably close to each other. The role of other explaining factors, such as calibration accuracy and experimental procedure, are discussed.

Keywords

Text entry Gaze input Performance Entry speed Error rate Comparative evaluation Longitudinal study 

Notes

Acknowledgments

I am grateful to Per Ola Kristensson for suggesting this experiment and to Päivi Majaranta, Poika Isokoski, Saila Ovaska and Oleg Špakov for discussions and comments on the manuscript. In particular, Oleg Špakov implemented the soft keyboard and the gaze tracking algorithm used in the experiment. The statistical analysis was done using I. Scott MacKenzie’s Java tools (http://www.yorku.ca/mack/RN-Anova.html). Finally, the participants in the experiment were committed and responsible – thank you very much!

This work was supported by the Academy of Finland (project MIPI).

References

  1. 1.
    Akkil, D., Isokoski, P., Kangas, J., Rantala, J., Raisamo, R.: TraQuMe: a tool for measuring the gaze tracking quality. In: Proceedings of the Symposium on Eye Tracking Research and Applications (ETRA 2014), pp. 11–18. ACM, New York (2014)Google Scholar
  2. 2.
    Isokoski, P., Linden, T.: Effect of foreign language on text transcription performance: Finns writing English. In: Proceedings of the Third Nordic Conference on Human-Computer Interaction (NordiCHI 2004), pp. 109–112. ACM, New York (2004)Google Scholar
  3. 3.
    MacKenzie, I.S., Soukoreff, R.W.: Phrase sets for evaluating text entry techniques. In: CHI 2003 Extended Abstracts on Human Factors in Computing Systems (CHI EA 2003), pp. 754–755. ACM, New York (2003)Google Scholar
  4. 4.
    Majaranta, P.: Text entry by eye gaze. Dissertations in Interactive Technology 11, University of Tampere (2009)Google Scholar
  5. 5.
    Majaranta, P., Ahola, U.-K., Špakov, O.: Fast gaze typing with an adjustable dwell time. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2009), pp. 357–360. ACM, New York (2009)Google Scholar
  6. 6.
    Majaranta, P., Aoki, H., et al. (eds.): Gaze Interaction and Applications of Eye Tracking: Advances in Assistive Technologies. IGI Global, Hershey (2012)Google Scholar
  7. 7.
    Majaranta, P., MacKenzie, I.S., Aula, A., Räihä, K.-J.: Effects of feedback and dwell time on eye typing speed and accuracy. Univ. Access Inf. Soc. 5, 199–208 (2006)CrossRefGoogle Scholar
  8. 8.
    Majaranta, P., Räihä, K.-J.: Text entry by gaze: utilizing eye-tracking. In: MacKenzie, I.S., Tanaka-Ishii, K. (eds.) Text Entry Systems: Mobility, Accessibility, Universality, pp. 175–187. Morgan Kaufmann, San Francisco (2007)CrossRefGoogle Scholar
  9. 9.
    Pedrosa, D., da Graça Pimentel, M., Wright, A., Truong, K.N.: Filteryedping: design challenges and user performance of dwell-free eye typing. ACM Trans. Accessible Comput. 6(1), 37 (2015). Article 3CrossRefGoogle Scholar
  10. 10.
    Räihä, K.-J., Ovaska, S.: An exploratory study of eye typing fundamentals: dwell time, text entry rate, errors, and workload. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2012), pp. 3001–3010. ACM, New York (2012)Google Scholar
  11. 11.
    Rough, D., Vertanen, K., Kristensson, P.O.: An evaluation of Dasher with a high-performance language model as a gaze communication method. In: Proceedings of the 2014 International Working Conference on Advanced Visual Interfaces (AVI 2014), pp. 169–176. ACM, New York (2014)Google Scholar
  12. 12.
    Sanchis-Trilles, G., Leiva, L.A.: A systematic comparison of 3 phrase sampling methods for text entry experiments in 10 languages. In: Proceedings of the 16th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI 2014), pp. 537–542. ACM, New York (2014)Google Scholar
  13. 13.
    Špakov, O.: iComponent – device-independent platform for analyzing eye movement data and developing eye-based applications. Dissertations in Interactive Technology 9, University of Tampere (2008). http://www.sis.uta.fi/~csolsp/downloads.php
  14. 14.
    Tuisku, O., Majaranta, P., Isokoski, P., Räihä, K.-J.: Now Dasher! Dash away! longitudinal study of fast text entry by eye gaze. In: Proceedings of the Symposium on Eye Tracking Research and Applications (ETRA 2008), pp. 19–26. ACM, New York (2008)Google Scholar
  15. 15.
    Urbina, M.H., Huckauf, A.: Alternatives to single character entry and dwell time selection on eye typing. In: Proceedings of the Symposium on Eye Tracking Research and Applications (ETRA 2010), pp. 315–322. ACM, New York (2010)Google Scholar
  16. 16.
    Wobbrock, J.O.: Measures of text entry performance. In: MacKenzie, I.S., Tanaka-Ishii, K. (eds.) Text Entry Systems: Mobility, Accessibility, Universality, pp. 47–74. Morgan Kaufmann, San Francisco (2007)CrossRefGoogle Scholar
  17. 17.
    Wobbrock, J.O., Rubinstein, J., Sawyer, M.W., Duchowski, A.T.: Longitudinal evaluation of discrete consecutive gaze gestures for text entry. In: Proceedings of the Symposium on Eye Tracking Research and Applications (ETRA 2008), pp. 11–18. ACM, New York (2008)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  1. 1.School of Information SciencesUniversity of TampereTampereFinland

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