The Effects of Typing Demand on Emotional Stress, Mouse and Keystroke Behaviours

  • Yee Mei Lim
  • Aladdin Ayesh
  • Martin Stacey
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 591)


Past research found that cognitive effort is related to emotion, which negative emotion may influence task performance. To enhance learning experience, it is important to have an effective technique to measure user’s emotional and motivational affects for designing an adaptive e-learning system, rather than using a subjective method that is less reliable and accurate. Keystroke and mouse dynamics analyses shed light on a better automated emotion recognition method as compared to physiological methods, as they are cheaper, non-invasive and can be easily set up. This research shows that unification of mouse and keyboard dynamics analyses could be useful in detecting emotional stress, particularly stress induced by time pressure, text length and language familiarity. The changes of mouse and keystroke behaviours of the students are found cohere with their task performance and stress perception. However anomalies in mouse and keystroke behaviours present when the students are pushed beyond their capabilities.


Emotional stress Keyboard dynamics Mouse dynamics Language familiarity Text length 


  1. 1.
    Sweller, J., Ayres, P., Kalyuga, S.: Cognitive Load Theory. Springer, Berlin (2011)CrossRefGoogle Scholar
  2. 2.
    Paas, F., Renkl, A., Sweller, J.: Cognitive load theory: instructional implications of the interaction between information structures and cognitive architecture. Instr. Sci. 32, 1–8 (2004)CrossRefGoogle Scholar
  3. 3.
    Beilock, S.L., Ramirez, G.: On the interplay of emotion and cognitive control: implications for enhancing academic achievement. Psychol. Learn. Motiv. Res. Theor. 55, 137 (2011)CrossRefGoogle Scholar
  4. 4.
    Kirschner, P.A.: Cognitive load theory: implications of cognitive load theory on the design of learning. Learn. Instr. 12, 1–10 (2002)CrossRefGoogle Scholar
  5. 5.
    Anastasi, A.: Psychological Testing. Macmillan, UK (1954)Google Scholar
  6. 6.
    Karasek, R.A.: Job demands, job decision latitude and mental strain: implications for job design. Adm. Sci. Q. 24, 285–308 (1979)CrossRefGoogle Scholar
  7. 7.
    Rijk, A.E., Le Blanc, P.M., Schaufeli, W.B., Jonge, J.: Active coping and need for control as moderators of the job demand–control model: effects on burnout. J. Occup. Organ. Psychol. 71, 1–18 (1998)CrossRefGoogle Scholar
  8. 8.
    Wahlström, J., Hagberg, M., Johnson, P.W., Svensson, J., Rempel, D.: Influence of time pressure and verbal provocation on physiological and psychological reactions during work with a computer mouse. Eur. J. Appl. Physiol. 87(3), 257–263 (2002)CrossRefGoogle Scholar
  9. 9.
    Heiden, M., Lyskov, E., Djupsjbacka, M., Hellstrm, F., Crenshaw, A.G.: Effects of time pressure and precision demands during computer mouse work on muscle oxygenation and position sense. Eur. J. Appl. Physiol. 94, 97–106 (2005)CrossRefGoogle Scholar
  10. 10.
    Pusara, M., Brodley, C.E.: User re-authentication via mouse movements. In: Proceedings of the 2004 ACM Workshop on Visualization and Data Mining for Computer Security, pp. 1–8. ACM, New York (2004)Google Scholar
  11. 11.
    Tsoulouhas, G., Georgiou, D., Karakos, A.: Detection of learner’s affective state based on mouse movements. J. Comput. 3, 9–18 (2011)Google Scholar
  12. 12.
    Vizer, L.M.: Detecting cognitive and physical stress through typing behavior. In: Proceedings of 27th International Conference Extended Abstracts Human Factors Computer System CHI EA 09, p. 3113 (2009)Google Scholar
  13. 13.
    Lim, Y.M., Ayesh, A., Stacey, M.: Detecting cognitive stress from keyboard and mouse dynamics during mental arithmetic. In: Science and Information Conference 2014. pp. 146–152. IEEE Xplore, London (2014)Google Scholar
  14. 14.
    Setz, C., Arnrich, B., Schumm, J., La Marca, R., Troster, G., Ehlert, U.: Discriminating stress from cognitive load using a wearable EDA device. Inf. Technol. Biomed. IEEE Trans. 14, 410–417 (2010)CrossRefGoogle Scholar
  15. 15.
    Sloan, R.P., Korten, J.B., Myers, M.M.: Components of heart rate reactivity during mental arithmetic with and without speaking. Physiol. Behav. 50, 1039–1045 (1991)CrossRefGoogle Scholar
  16. 16.
    Khan, M.M., Ward, R.D., Ingleby, M.: Classifying pretended and evoked facial expressions of positive and negative affective states using infrared measurement of skin temperature. ACM Trans. Appl. Percept. 6, 1–22 (2009)CrossRefGoogle Scholar
  17. 17.
    Tobias, S., Abramson, T.: Interaction among anxiety, stress, response mode, and familiarity of subject matter on achievement from programmed instruction. J. Educ. Psychol. 62, 357 (1971)CrossRefGoogle Scholar
  18. 18.
    Hulme, C., Maughan, S., Brown, G.D.A.: Memory for familiar and unfamiliar words: evidence for a long-term memory contribution to short-term memory span. J. Mem. Lang. 30, 685–701 (1991)CrossRefGoogle Scholar
  19. 19.
    Davidson, J.E., Sternberg, R.J.: The Psychology of Problem Solving. Cambridge University press, Cambridge (2003)Google Scholar
  20. 20.
    Robinson, J.A., Liang, V.M.: Computer user verification using login string keystroke dynamics. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 28, 236–241 (1998)CrossRefGoogle Scholar
  21. 21.
    Boechat, G.C., Ferreira, J.C., Carvalho Filho, E.: Authentication personal. In: International Conference on Intelligent and Advanced Systems, 2007 (ICIAS 2007), pp. 254–256 (2007)Google Scholar
  22. 22.
    Lv, H.-R., Lin, Z.-L., Yin, W.-J., Dong, J.: Emotion recognition based on pressure sensor keyboards. In: IEEE International Conference on Multimedia and Exposure 2008, pp. 1089–1092 (2008)Google Scholar
  23. 23.
    Teh, P.S., Yue, S., Teoh, A.B.J.: Improving keystroke dynamics authentication system via multiple feature fusion scheme. In: International Conference on Cyber Security, Cyber Warfare and Digital Forensic (CyberSec). pp. 277–282 (2012)Google Scholar
  24. 24.
    Eswari, N., Sundarapandiyan, S., Vennila, P., Umamaheswari, R., Jothilakshmi, G.: Keystroke biometrics with number-pad input using hybridization of adaboost with random forest. In: International Conference on Advances in Engineering, Science and Management (ICAESM). pp. 105–109 (2012)Google Scholar
  25. 25.
    Giot, R., Rosenberger, C., Dorizz, B.: Can chronological information be used as a soft biometric in keystroke dynamics? In: Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 7–10 (2012)Google Scholar
  26. 26.
    Giot, R., El-Abed, M., Rosenberger, C.: Web-based benchmark for keystroke dynamics biometric systems: a statistical analysis. In: Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). pp. 11–15 (2012)Google Scholar
  27. 27.
    Gunetti, D., Picardi, C.: Keystroke analysis of free text. ACM Trans. Inf. Syst. Secur. 8, 312–347 (2005)CrossRefGoogle Scholar
  28. 28.
    Shimshon, T., Moskovitch, R., Rokach, L., Elovici, Y.: Clustering di-graphs for continuously verifying users according to their typing patterns. 2010 IEEE 26th Convention of Electrical and Electronics Engineers in Israel (IEEEI), pp. 445–449 (2010)Google Scholar
  29. 29.
    Selye, H.: The Stress in Life. McGraw-Hill, New York (1956)Google Scholar
  30. 30.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Applied Sciences and ComputingTunku Abdul Rahman University CollegeKuala LumpurMalaysia
  2. 2.Faculty of TechnologyDe Montfort UniversityLeicesterUK

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