Beyond heat maps: mining common swipe gestures

ABSTRACT

Heat maps are a common tool in research to visualize human-computer-interaction. Despite being widely used to track navigation, clicks, cursor moves or eye gaze, heat maps have not yet been explored as a means to understand users' gestural interaction with mobile devices. This understanding is particularly relevant in the case of older adult users who are often novice users and may also struggle with accuracy in gesture performance. This paper explores the application of the DBScan clustering algorithm to uncover the most relevant swipe gestures in a data sets containing the user interaction of two mobile applications. An intuitive visualization of the clustering results will be presented and compared in a case study with a heat map visualization, discussing the novelty and usefulness of these visualizations for user behaviour and usability studies.

References

1. Holzinger, A., Searle, G., and Nischelwitzer, A. 2007. On some aspects of improving mobile applications for the elderly. In Proceedings of the 4th international conference on Universal access in human computer interaction: coping with diversity. Springer-Verlag Berlin, Heidelberg, 923--932.
2. Leitão, R. 2012. Creating mobile gesture-based interaction design patterns for older adults: A study of tap and swipe gestures with Portuguese seniors. MSc Dissertation. Faculdade de Engenharia da Universidade do Porto.
3. Hollinworth, N. and Hwang, F. 2011. Investigating familiar interactions to help older adults learn computer applications more easily. In Proceedings of BCS-HCI'11. British computer Society Swinton, UK, 473--478.
4. Nielsen, C. M., Overgaard, M., Pedersen, M. B., Stage, J., and Stenild, S. 2006. It's worth the hassle!: the added value of evaluating the usability of mobile systems in the field. In Proceedings of the 4th Nordic conference on Human-computer interaction: changing roles (Oslo, Norway, October 14--18, 2006). NordiCHI'06. ACM, New York, NY, 272--280. DOI=http://dx.doi.org/10.1145/1182475.1182504
5. Lettner, F. and Holzmann, C. 2012. Heat maps as a usability tool for multi-touch interaction in mobile applications. In Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia (Ulm, Germany, December 4--6, 2012). MUM'12. ACM, New York, NY, Article no. 49. DOI=http://dx.doi.org/10.1145/2406367.2406427
6. Ellis, K. K. E. 2009. Eye tracking metrics for workload estimation in flight deck operations. MSc Dissertation. University of Iowa.
7. Isomäki, H. and Pekkola, S. 2011. Introduction: Reframing humans and information systems. In Reframing humans in information systems development, H. Isomäki, and S. Pekkola, Eds. Springer, London, 1--14.
8. Huang, J. and Diriye, A. 2012. Web user interaction mining from touch-enabled mobile devices. In Proceedings of HCIR 2012. (Cambridge).
9. Jin, Z. X., Plocher, T., and Kiff, L. 2007. Touch screen user interfaces for older adults: Button size and spacing. In Proceedings of the 4th international conference on Universal access in human computer interaction: coping with diversity. Springer-Verlag, Berlin, 933--941.
10. Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Portland, OR, 226--231.
11. Guo, Q., Jin, H., Lagun, D., Yuan, S., and Agichtein, E. 2013. Mining touch interaction data on mobile devices to predict web search result relevance. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (Dublin, Ireland, July 28-August 1, 2013). SIGIR'13. ACM, New York, NY, 153--161. DOI=http://dx.doi.org/10.1145/2484028.2484100
12. Ankerst, M., Breunig, M. M., Kriegel, H.-P., and Sander, J. 1999. OPTICS: Ordering points to identify the clustering structure. In Proceedings of the 1999 ACM SIGMOD international conference on Management of data (Philadelphia, USA, June 1--3, 1999). SIGMOD'99. ACM, New York, NY, 49--60. DOI=http://dx.doi.org/10.1145/304182.304187

Index Terms

• Human-centered computing

  • Human computer interaction (HCI)