User Interaction Forensics

Detecting and Interpreting the User’s Footprints during Touch Interaction
  • Kai Breiner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8012)


The foundation of self-adaptive systems is sound elicitation of the input for the adaptation algorithm. If the input of the adaptation is not reliable, the resulting adaptation will not be reliable either. Especially if the aim is to adapt to the user, the information probably stems from unobtrusive measures but still needs to be reliable. Thus, this paper describes a controlled experiment conducted to investigate in four hypotheses how to make miscellaneous interaction information (which is available anyway) interpretable. These four hypotheses concern three aspects: precision of the interaction step, bias according to right-/left-handedness, and bias of the interaction element. A total of 33 participants were involved. All four hypotheses could be strengthened at a high level of significance.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Breiner, K.: AssistU - A framework for user interaction forensics. TU Kaiserslautern, Kaiserslautern. PhD-Thesis (2013)Google Scholar
  2. 2.
    Card, S.K., Moran, T.P., Newell, A.: The Model Human Processor: An engineering model of human performance. In: Boff, K.R., Kaufmann, L., Thomas, J.P. (eds.) Handbook of Perception and Human Performance. Wiley and Sons, New York (1986)Google Scholar
  3. 3.
    Esteves, M., Komischke, T., Zapf, S., Weiss, A.: Applied user performance modeling in industry – A case study from medical imaging. In: Duffy, V.G. (ed.) HCII 2007 and DHM 2007. LNCS, vol. 4561, pp. 576–585. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Gajos, K.: Automatically Generating Personalized User Interfaces. University of Washington, Washington. PhD-Thesis (2008)Google Scholar
  5. 5.
    Gajos, K., Czerwinski, M., Tan, D.S., Weld, D.S.: Exploring the Design Space For Adaptive Graphical User Interfaces. In: Proceedings of AVI 2006, Venice, Italy (2006)Google Scholar
  6. 6.
    Gajos, K., Long, J.J., Weld, D.S.: Automatically Generating Custom User Interfaces for Users With Physical Disabilities. In: ASSETS 2006, Portland, OR (2006)Google Scholar
  7. 7.
    Gajos, K., Wobbrock, J.O., Weld, D.S.: Automatically Generating User Interfaces Adapted To Users’ Motor And Vision Capabilities. In: Proceedings of UIST 2007, Newport, RI, USA (2007)Google Scholar
  8. 8.
    Ghazarian, A., Noorhosseini, S.M.: Automatic detection of users’ skill levels using high-frequency user interface events. In: Journal User Modeling and User-Adapted Interaction. Kluwer Academic Publishers, Hingham (2010)Google Scholar
  9. 9.
    Hernández, J. A., Larrabeiti, D., Strnad, O., Schmidt, A.: Prototype for user context management (2011)Google Scholar
  10. 10.
    Hess, S., Maier, A., Trapp, M.: Differentiating between successful and less successful products by using MAInEEAC - a model for interaction characterization. In: Jacko, J.A. (ed.) Human-Computer Interaction, Part I, HCII 2011. LNCS, vol. 6761, pp. 238–247. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    MyUI - Mainstreaming Accessibility through Synergistic User Modelling and Adaptability, (retrieved February 12, 2012)

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kai Breiner
    • 1
  1. 1.Fraunhofer Institute for Experimental Software Engineering IESEKaiserslauternGermany

Personalised recommendations