Emotion Recognition through Keystroke Dynamics on Touchscreen Keyboards

Matthias Trojahn, Florian Arndt, Markus Weinmann, Frank Ortmeier

Abstract

Automatic emotion recognition through computers offers a lot of advantages, as the interaction between human and computers can be improved. For example, it is possible to be responsive to anger or frustration of customers automatically while working with a webpage. Mouse cursor movements and keystroke dynamics were already used and examined for such a recognition on conventional keyboards. The aim of this work is to investigate keystroke dynamics on touchscreen keyboards which gets a cumulative relevance through the increasingly further circulation of smartphones and tablets. Furthermore, it is possible to record additional information like pressure and size of keystrokes. This could increase the recognition rate for emotions. In order to record the keystroke dynamics, an application and keyboard layout for Android OS were developed. In addition, hypotheses were established on the basis of Yerkes-Dodson-Law and Flow theory and besides, a study with 152 test persons for the data collection was implemented. Subsequently, a data evaluation with the SPSS software was accomplished. Most of the hypotheses were confirmed and the results of the study show that emotions can be explained by the keystroke dynamics and recognized in this way.

References

  1. Alepis, E. and Virvou, M. (2006). Emotional intelligence: constructing user stereotypes for affective bimodal interaction. In Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I, KES'06, pages 435-442, Berlin, Heidelberg. Springer-Verlag.
  2. Amberg, M., Fischer, S., and Rößler, J. (2003). Biometrische Verfahren - Studie zum State of the Art. Technical report, Friedrich-AlexanderUniversität Erlangen-Nürnberg.
  3. Bradley, M. M. and Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1):49 - 59.
  4. Buchoux, A. and Clarke, N. L. (2008). Deployment of keystroke analysis on a smartphone. In Proceedings of the 6th Australian Information Security & Management Conference.
  5. Cohen, S., Doyle, W. J., Turner, R. B., Alper, C. M., and Skoner, D. P. (2003). Emotional style and susceptibility to the common cold. Psychosomatic Medicine, 65(4):652-657.
  6. Csikszentmihalyi, M. (1975). Play and intrinsic rewards. Journal of Humanistic Psychology, 15(3):41-63.
  7. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, (Sept.):319-339.
  8. Joyce, R. and Gupta, G. (1990). Identity authentication based on keystroke latencies. Commun. ACM, 33(2):168-176.
  9. Khanna, P. and M.Sasikumar (2010). Recognising emotions from keyboard stroke pattern. International Journal of Computer Applications, 11(9):1-5.
  10. Lazarus, R. S., Deese, J., and Osler, S. F. (1952). The effects of psychological stress upon performance. Psychological Bulletin 49, 4:293-317.
  11. Lazarus, R. S. P. (2006). Stress and Emotion: A New Synthesis. Springer Series. Springer Publishing Company.
  12. Maehr, W. (2008). eMotion: Estimation of User's Emotional State by Mouse Motions. VDM Verlag, Saarbrücken, Germany.
  13. Mehrabian, A. (1970). A semantic space for nonverbal behavior. Journal of Consulting and Clinical Psychology, 35 (2):248-257.
  14. Monrose, F. and Rubin, A. (1997). Authentication via keystroke dynamics. In Proceedings of the 4th ACM conference on Computer and communications security, CCS 7897, pages 48-56, New York, NY, USA. ACM.
  15. Moon, J.-W. and Kim, Y.-G. (2001). Extending the tam for a world-wide-web context. Information and Management, 38(4):217 - 230.
  16. Selye, H. (1936). A syndrome produced by diverse nocuous agents. Nature, 138:32.
  17. Selye, H. (1975). Stress and distress. Comprehensive Therapy, 1 (8):9-13.
  18. Trojahn, M. and Ortmeier, F. (2012). Biometric authentication through a virtual keyboard for smartphones. In International Journal of Computer Science & Information Technology (IJCSIT).
  19. Wagner, U., Gais, S., and Born, J. (2001). Emotional memory formation is enhanced across sleep intervals with high amounts of rapid eye movement sleep. Learning & Memory, 8(2):112-119.
  20. Yerkes, R. M. and Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology, 18(5):459-482.
  21. Zimmermann, P., Guttormsen, S., Danuser, B., and Gomez, P. (2003). Affective computing-a rationale for measuring mood with mouse and keyboard. International Journal of Occupational Safety and Ergonomics.
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Paper Citation


in Harvard Style

Trojahn M., Arndt F., Weinmann M. and Ortmeier F. (2013). Emotion Recognition through Keystroke Dynamics on Touchscreen Keyboards . In Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 3: ICEIS, ISBN 978-989-8565-61-7, pages 31-37. DOI: 10.5220/0004415500310037


in Bibtex Style

@conference{iceis13,
author={Matthias Trojahn and Florian Arndt and Markus Weinmann and Frank Ortmeier},
title={Emotion Recognition through Keystroke Dynamics on Touchscreen Keyboards},
booktitle={Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 3: ICEIS,},
year={2013},
pages={31-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004415500310037},
isbn={978-989-8565-61-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 3: ICEIS,
TI - Emotion Recognition through Keystroke Dynamics on Touchscreen Keyboards
SN - 978-989-8565-61-7
AU - Trojahn M.
AU - Arndt F.
AU - Weinmann M.
AU - Ortmeier F.
PY - 2013
SP - 31
EP - 37
DO - 10.5220/0004415500310037