International Conference on Computers for Handicapped Persons

ICCHP 2014: Computers Helping People with Special Needs pp 248-255 | Cite as

Detection and Utilization of Emotional State for Disabled Users

  • Yehya Mohamad
  • Dirk T. Hettich
  • Elaina Bolinger
  • Niels Birbaumer
  • Wolfgang Rosenstiel
  • Martin Bogdan
  • Tamara Matuz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8547)

Abstract

In this paper, we present an experimental approach to design systems sensitive to emotion. We describe a system for the detection of emotional states based on physiological signals and an application use case utilizing the detected emotional state. The application is an emotion management system to be used for the support in the improvement of life conditions of users suffering from cerebral palsy (CP). The system presented here combines effectively biofeedback sensors and a set of software algorithms to detect the current emotional state of the user and to react to them appropriately.

Keywords

Affective Computing Machine Learning Algorithm Disabled Person Context Emotion Emotion Management User Interface E-Learning Web-Based 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bradley, M.M., Lang, P.J.: The International Affective Digitized Sounds, 2nd edn. (IADS-2): Affective Ratings of Sounds and Instruction Manual NIMH Center for the Study of Emotion and Attention 2007 (2007)Google Scholar
  2. 2.
    Goleman, D.: Emotional Intelligence. Bantam Books, New York (1995)Google Scholar
  3. 3.
    Kolb, H.J.: Informationsverarbeitung in der Messtechnik zur Diagnose und Qualitätssicherung. In: 43rd International Scientific Colloquium, Technical University of Ilmenau, September 21-24 (1998)Google Scholar
  4. 4.
    Mohamad, Y.: Integration of Emotional Intelligence in Interface Agents: The example of a Training Software for Learning-Disabled Children (2005) ISBN-13: 978-3832244637Google Scholar
  5. 5.
    Picard, R.: IBM Systems Journal 39(3&4) (2000)Google Scholar
  6. 6.
    Rigoll, G., Müller, S.: Statistical Pattern Recognition Techniques for Multimodal Human Computer Interaction and Multimedia Information Processing. In: Survey Paper, Int. Workshop “Speech and Computer”, Moscow, Russia, October, pp. 60–69 (1999)Google Scholar
  7. 7.
    Shi, Y., et al.: Personalized Stress Detection from Physiological Measurements. In: International Symposium on Quality of Life Technology (2010)Google Scholar
  8. 8.
    Villarejo, M.V., Zapirain, B.G., Zorrilla, A.M.: A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee (2012), www.mdpi.com/journal/sensors-2012 sensors ISSN 1424-8220
  9. 9.
    Cuthbert, B.N., Schupp, H.T., Bradley, M.M., Birbaumer, N., Lang, P.J.: Brain potentials in affective picture processing: covariation with autonomic arousal and affective report. Biol. Psychol. 52(2), 95–111 (2000)CrossRefGoogle Scholar
  10. 10.
    Davidson, R.J., Ekman, P., Saron, C.D., Senulis, J.A., Friesen, W.V.: Approach-withdrawal and cerebral asymmetry: emotional expression and brain physiology. I. J. Pers. Soc. Psychol. 58(2), 330–341 (1990)CrossRefGoogle Scholar
  11. 11.
    Gershenfeld, N.A.: When Things Start to Think. Owl Books, New York (2000)Google Scholar
  12. 12.
    Sharma, N., Gedeon, T.: Objective measures, sensors and computational techniques for stress recognition and classification: a survey. Comput. Methods Programs Biomed. 108(3), 1287–1301 (2012)CrossRefGoogle Scholar
  13. 13.
    Garay, N., Cearreta, I., López, J.M., Fajardo, I.: Assistive technology and affective mediation. An Interdiscip. J. Humans ICT Environ. 2(1), 55–83 (2006)CrossRefGoogle Scholar
  14. 14.
    Bethel, C.L., Salomon, K., Murphy, R.R., Burke, J.L.: Survey of Psychophysiology Measurements Applied to Human-Robot Interaction. In: RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 732–737 (2007)Google Scholar
  15. 15.
    Calvo, R.A., D’Mello, S.: Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications. IEEE Trans. Affect. Comput. 1(1), 18–37 (2010)CrossRefGoogle Scholar
  16. 16.
    Calvo, R., Brown, I., Scheding, S.: Effect of experimental factors on the recognition of affective mental states through physiological measures. In: Nicholson, A., Li, X. (eds.) AI 2009. LNCS, vol. 5866, pp. 62–70. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Bradley, M.M., Lang, P.J.: The International Affective Picture System (IAPS) in the study of emotion and attention. In: Coan, J.A., Allen, J.J.B. (eds.) Handbook of Emotion Elicitation and Assessment, pp. 29–46. Oxford University Press (2007)Google Scholar
  18. 18.
    Bradley, M.M., Lang, P.J.: Measuring emotion: the Self-Assessment Manikin and the Semantic Differential. J. Behav. Ther. Exp. Psychiatry 25(1), 49–59 (1994)CrossRefGoogle Scholar
  19. 19.
    Schlogl, A., et al.: A fully automated correction method of EOG artifacts in EEG recordings. Clin. Neurophysiol. 118(1), 98–104 (2007), doi:10.1016/j.clinph.2006.09.003MathSciNetCrossRefGoogle Scholar
  20. 20.
    Chang, C., Lin, C.: LIBSVM: A Library for Support Vector Machines. ACM Trans. Intell. Syst. Technol. 2(3) (2011) DOI citeulike-article-id: 9306445Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yehya Mohamad
    • 1
  • Dirk T. Hettich
    • 2
    • 3
  • Elaina Bolinger
    • 3
  • Niels Birbaumer
    • 3
  • Wolfgang Rosenstiel
    • 2
  • Martin Bogdan
    • 2
  • Tamara Matuz
    • 3
  1. 1.Schloss BirlinghovenFraunhofer Institute for Applied Information Technology (FIT)Sankt AugustinGermany
  2. 2.Department of Computer Engineering, Faculty of ScienceEberhard-Karls-University of TübingenGermany
  3. 3.Institute of Medical Psychology and Behavioral NeurobiologyEberhard-Karls-University of TübingenGermany

Personalised recommendations