skip to main content
10.1145/2556288.2557274acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

Robot gestures make difficult tasks easier: the impact of gestures on perceived workload and task performance

Authors Info & Claims
Published:26 April 2014Publication History

ABSTRACT

Gestures are important non-verbal signals in human communication. Research with virtual agents and robots has started to add to the scientific knowledge about gestures but many questions with respect to the use of gestures in human-computer interaction are still open. This paper investigates the influence of robot gestures on the users' perceived workload and task performance (i.e. information recall) in a direction-giving task. We conducted a 2 x 2 (robot gestures vs. no robot gestures x easy vs. difficult task) experiment. The results indicate that robot gestures increased user performance and decreased perceived workload in the difficult task but not in the easy task. Thus, robot gestures are a promising means to improve human-robot interaction particularly in challenging tasks.

References

  1. Bergmann, K., Eyssel, F. A., and Kopp, S. A second chance to make a first impression' how appearance and nonverbal behavior affect perceived warmth and competence of virtual agents over time. M. Walker, M. Neff, A. Paiva, and Y. Nakano, Eds., Proceedings of the 12th International Conference on Intelligent Virtual Agents, Springer (2012), 126--138. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Bergmann, K., Kahl, S., and Kopp, S. Modeling the semantic coordination of speech and gesture under cognitive and linguistic constraints. In Intelligent Virtual Agents, R. Aylett, B. Krenn, C. Pelachaud, and H. Shimodaira, Eds., vol. 8108 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013, 203--216.Google ScholarGoogle Scholar
  3. Breazeal, C., Kidd, C., Thomaz, A., Hoffman, G., and Berlin, M. Effects of nonverbal communication on efficiency and robustness in human-robot teamwork. In Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on (2005), 708--713.Google ScholarGoogle ScholarCross RefCross Ref
  4. Buisine, S., Abrilian, S., and Martin, J.-C. From brows to trust. Kluwer Academic Publishers, Norwell, MA, USA, 2004, ch. Evaluation of multimodal behaviour of embodied agents, 217--238. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Cassell, J. Embodied conversational agents. MIT Press, Cambridge, MA, USA, 2000, ch. Nudge nudge wink wink: elements of face-to-face conversation for embodied conversational agents, 1--27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Chidambaram, V., Chiang, Y.-H., and Mutlu, B. Designing persuasive robots: how robots might persuade people using vocal and nonverbal cues. In Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction, HRI '12, ACM (New York, NY, USA, 2012), 293--300. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Field, A. Discovering statistics using SPSS. Sage publications, 2009.Google ScholarGoogle Scholar
  8. Fox, M. L., Dwyer, D. J., and Ganster, D. C. Effects of stressful job demands and control on physiological and attitudinal outcomes in a hospital setting. Academy of Management Journal 36, 2 (1993), 289--318.Google ScholarGoogle Scholar
  9. Goldin-Meadow, S. Hearing Gesture: How Our Hands Help Us Think. Belknap Press of Harvard University Press, 2005.Google ScholarGoogle Scholar
  10. Gu, E., and Badler, N. I. Visual attention and eye gaze during multiparty conversations with distractions. In IVA, J. Gratch, M. Young, R. Aylett, D. Ballin, and P. Olivier, Eds., vol. 4133 of Lecture Notes in Computer Science, Springer (2006), 193--204. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Häring, M., Eichberg, J., and André, E. Studies on grounding with gaze and pointing gestures in human-robot-interaction. In Social Robotics, S. Ge, O. Khatib, J.-J. Cabibihan, R. Simmons, and M.-A. Williams, Eds., vol. 7621 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012, 378--387. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Hart, S., and Staveland, L. Development of nasa-tlx (task load index): Results of empirical and theoretical research. In Human mental workload, P. A. Hancock and N. Meshkati (Eds.), Amsterdam: Elsevier (1988), 139--183.Google ScholarGoogle Scholar
  13. Hart, S. G. Nasa-task load index (nasa-tlx); 20 years later. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 50, 9 (2006), 904--908.Google ScholarGoogle ScholarCross RefCross Ref
  14. Hato, Y., Satake, S., Kanda, T., Imai, M., and Hagita, N. Pointing to space: modeling of deictic interaction referring to regions. In Proceedings of the Conference on Human-Robot Interaction, P. J. Hinds, H. Ishiguro, T. Kanda, and P. H. K. Jr., Eds., ACM (2010), 301--308. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Ilies, R., Dimotakis, N., and De Pater, I. E. Psychological and physiological reactions to high workloads: Implications for well-being. Personnel Psychology 63, 2 (2010), 407--436.Google ScholarGoogle ScholarCross RefCross Ref
  16. Iverson, J. M., Capirci, P., Longobardi, E., and Caselli, M. C. Gesturing in mother-child interaction. Cognitive Development 14 (1999), 57--75.Google ScholarGoogle ScholarCross RefCross Ref
  17. Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., and Hagita, N. A communication robot in a shopping mall. Robotics, IEEE Transactions on 26, 5 (2010), 897--913. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Kendon, A. Gesture and speech: How they interact. In Nonverbal Interaction (Sage Annual Reviews of Communication, Volume 11) J. Wiemann and R. Harrison, Eds. Sage Publications, Beverly Hills, California, 1983, 13--46.Google ScholarGoogle Scholar
  19. Kendon, A. Gesture - Visible Action as Utterance. Cambridge University Press, Cambridge, 2004.Google ScholarGoogle Scholar
  20. Kipp, M., Neff, M., Kipp, K. H., and Albrecht, I. Towards natural gesture synthesis: Evaluating gesture units in a data-driven approach to gesture synthesis. In Proceedings of the 7th international conference on Intelligent Virtual Agents, IVA '07, Springer-Verlag (Berlin, Heidelberg, 2007), 15--28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Kita, S. Pointing: Where Language, Culture, and Cognition Meet. PSYCHOLOGY Press, 2003.Google ScholarGoogle Scholar
  22. McNeil, N., Alibali, M., and Evans, J. The role of gesture in children's comprehension of spoken language: now they need it, now they don't. Journal of Nonverbal Behavior 24, 2 (2000), 131--150.Google ScholarGoogle ScholarCross RefCross Ref
  23. McNeill, D. Hand and Mind: What Gestures Reveal about Thought. University of Chicago Press, Chicago, 1992.Google ScholarGoogle Scholar
  24. Miller, G. A. The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review 63, 2 (March 1956), 81--97.Google ScholarGoogle ScholarCross RefCross Ref
  25. Ng-Thow-Hing, V., Luo, P., and Okita, S. Y. Synchronized gesture and speech production for humanoid robots. In IROS, IEEE (2010), 4617--4624.Google ScholarGoogle Scholar
  26. Okuno, Y., Kanda, T., Imai, M., Ishiguro, H., and Hagita, N. Providing route directions: design of robot's utterance, gesture, and timing. In Proceedings of the 4th ACM/IEEE international conference on Human robot interaction, HRI '09, ACM (New York, NY, USA, 2009), 53--60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Pelachaud, C. Studies on gesture expressivity for a virtual agent. Speech Commun. 51, 7 (July 2009), 630--639. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Salem, M., Eyssel, F., Rohlfing, K., Kopp, S., and Joublin, F. To err is human(-like): Effects of robot gesture on perceived anthropomorphism and likability. International Journal of Social Robotics 5, 3 (2013), 313--323.Google ScholarGoogle ScholarCross RefCross Ref
  29. Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., and Goodrich, M. Common metrics for human-robot interaction. In Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction, HRI '06, ACM (New York, NY, USA, 2006), 33--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Vitense, H. S., Jacko, J. A., and Emery, V. K. Multimodal feedback: an assessment of performance and mental workload. Ergonomics 46, 1--3 (2003), 68--87. PMID: 12554399.Google ScholarGoogle ScholarCross RefCross Ref
  31. Yoshiike, Y., Silva, P. R. S. D., and Okada, M. Mawari: A social interface to reduce the workload of the conversation. In ICSR, B. Mutlu, C. Bartneck, J. Ham, V. Evers, and T. Kanda, Eds., vol. 7072 of Lecture Notes in Computer Science, Springer (2011), 11--20. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Robot gestures make difficult tasks easier: the impact of gestures on perceived workload and task performance

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
      April 2014
      4206 pages
      ISBN:9781450324731
      DOI:10.1145/2556288

      Copyright © 2014 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 26 April 2014

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      CHI '14 Paper Acceptance Rate465of2,043submissions,23%Overall Acceptance Rate6,199of26,314submissions,24%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader