How Social Robots Make Older Users Really Feel Well – A Method to Assess Users’ Concepts of a Social Robotic Assistant

  • Tobias Körtner
  • Alexandra Schmid
  • Daliah Batko-Klein
  • Christoph Gisinger
  • Andreas Huber
  • Lara Lammer
  • Markus Vincze
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7621)

Abstract

The present study explored a workshop method including questionnaires but also creative, implicit methods as a new way of uncovering users’ implicit concepts of a helper and supporting creative answers by users. Eight older (70+) and ten younger (<70) participants collaborated in the workshops. They filled in a questionnaire and completed a picture association activity as well as a creative modelling unit. The word ‘robot’ was not used in the entire workshops to prevent users from directly thinking of robot stereotypes. Results demonstrated that picture associations cause a higher amount of answers regarding features of a ‘helper’ than direct questionnaire items. These results can be translated to the field of social robotics. According to the findings, users preferred a structure of the helper that featured arms, some kind of body and a head they could talk to. Most of all, picture associations played an important role in revealing the individual concepts of users. These results suggest that the method of implicit questioning is a useful additional approach in the assessment of user requirements and human-robot-interaction research.

Keywords

social robotics human-robot interaction assistive technology user requirement assessment methodology support for older people 

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References

  1. 1.
    European Commission–Eurostat: Key figures on Europe 2007/2008 edition. Luxembourg: Office for Official Publications of the European Communities (2008)Google Scholar
  2. 2.
    Dias, N., Kempen, G., Todd, C.J.: The German version of the Falls Efficacy Scale- International Version (FES-I). Gerontol Geriatr 39, 297–300 (2006)CrossRefGoogle Scholar
  3. 3.
    Goodrich, M.A., Schulz, A.C.: Human-robot interaction: a survey. Foundations and Trends in Human-Computer Interaction 1(3) (2007)Google Scholar
  4. 4.
    Dillon, A.: User acceptance of information technology. In: Karwowski, W. (ed.) Encyclopedia of Human Factors and Ergonomics. Taylor and Francis, London (2001)Google Scholar
  5. 5.
    Lee, M.K., Forlizzi, J., Kiesler, S., Rybski, P., Antanitis, J., Savetsila, S.: Personalization in HRI: A longitudinal field experiment. In: HRI 2012 Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 319–326 (2012)Google Scholar
  6. 6.
    Heinecke, A.M.: Mensch-Computer-Interaktion Basiswissen für Entwickler und Gestalter, 2nd edn. Springer, Berlin (2012)Google Scholar
  7. 7.
    Vincenzi, C., Spirig, R.: Die Bedürfnisse der Patienten stehen im Mittelpunkt. Managed Care 8, 12–14 (2006)Google Scholar
  8. 8.
    Lammer, L., Huber, A., Zagler, W., Vincze, M.: Mutual-Care: Users will love their imperfect social assistive robots. Work-In-Progress Proceedings of the International Conference on Social Robotics 2011, Amsterdam, the Netherlands, November 24- 25 (2011)Google Scholar
  9. 9.
    Lee, S., Lau, I., Kiesler, S., Chiu, C.: Human Mental Models of Humanoid Robots. In: Proceedings of the IEEE International Conference on Robotics and Automation (2005)Google Scholar
  10. 10.
    Heerink, M., Kröse, B., Evers, V., Wielinga, B.: Relating conversational expressiveness to social presence and acceptance of an assistive social robot. Springerlink 14(1), 77–84 (2009)Google Scholar
  11. 11.
    Kuo, I.H., Rabindran, J.M., Broadbent, E., Lee, Y.I., Kerse, N., Stafford, R.M.Q., MacDonald, B.A.: Age and gender factors in user acceptance of healthcare robots. The University of Auckland, New Zealand (2009)Google Scholar
  12. 12.
    Beer, J.M., Smarr, C.A., Chen, T.L., Prakash, A., Mitzner, T.L., Kemp, C.C., Rogers, W.A.: The domesticated robot: design guidelines for assisting older adults to age in place. In: HRI 2012 Proceedings of the Seventh Annual ACM/IEEE International Conference on Human -Robot Interaction, pp. 335–342 (2012)Google Scholar
  13. 13.
    Hirsch, T., Forlizzi, J., Hyder, E., Goetz, J., Kurtz, C., Stroback, J.: The ELDER project: social, emotional, and environmental factors in the design of eldercare technologies. In: CUU 2000 Proceedings on the 2000 Conference on Universal Usability, pp. 72–79 (2000)Google Scholar
  14. 14.
    Wu, Y.H., Fassert, C., Rigaud, A.-S.: Designing robots fort he elderly: appearance issue and beyond. Archives of Gerontology and Geriatrics 54, 121–126 (2012)CrossRefGoogle Scholar
  15. 15.
    Oestreicher, L., Severinson-Eklundh, K.: User Expectations on Human-Robot Cooperation. In: The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 91–96 (2006)Google Scholar
  16. 16.
    Meyer, S.: Mein Freund der Roboter. Servicerobotik für ältere Menschen - eine Antwort auf den demographischen Wandel? Institut für Sozialforschung und Projektberatung GmbH, Berlin (2011)Google Scholar
  17. 17.
    Riessman, F.: The ’helper’ therapy principle. Social Work 10(2), 27–32 (1965)Google Scholar
  18. 18.
    Paro Therapeutic Robot, http://www.parorobots.com
  19. 19.
    Murray, H.A.: Thematic Apperception Test. Harvard University Press, Cambridge (1943)Google Scholar
  20. 20.
    Broadbent, E., Tamagawa, R., Kerse, N., Knock, B., Patience, A., MacDonald, B.: Retirement home staff and residents’ preferences for healthcare robots. In: The 18th IEEE International Symposium on Robot and Human Interactive Communication, RO- MAN, pp. 645–650 (2009)Google Scholar
  21. 21.
    Mason, M., Lopes, M.: Robot self-initiative and personalization by learning through repeated interactions. In: HRI 2011 Proceedings of the 6th International Conference on Human-Robot Interaction, pp. 433–440 (2011)Google Scholar
  22. 22.
    Broadbent, E., Lee, Y.I., Stafford, R.Q., Han Kuo, I., MacDonald, B.A.: Mental Schemas of Robots as more Human-like are associated with higher blood pressure and negative emotions in a human-robot interaction. International Journal of Social Robotics 3(3), 291–297 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tobias Körtner
    • 1
  • Alexandra Schmid
    • 1
  • Daliah Batko-Klein
    • 1
  • Christoph Gisinger
    • 1
    • 2
  • Andreas Huber
    • 3
  • Lara Lammer
    • 3
  • Markus Vincze
    • 3
  1. 1.Academy for Ageing Research at Haus der BarmherzigkeitViennaAustria
  2. 2.Donauuniversität KremsKremsAustria
  3. 3.ACINVienna University of TechnologyViennaAustria

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