Enhancing Human Robot Interaction Through Social Network Interfaces: A Case Study

  • Laura Fiorini
  • Raffaele Limosani
  • Raffaele Esposito
  • Alessandro Manzi
  • Alessandra Moschetti
  • Manuele Bonaccorsi
  • Filippo Cavallo
  • Paolo Dario
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9170)

Abstract

Recently we have assisted to the rise of different Social Networks, and to the growth of robots for home applications, which represent the second big market opportunity. The use and the integration of robotics services in our daily life is strictly correlated with their usability and their acceptability. Particularly, their ease of use, among other issues, is linked to the simplicity of the interface the user has to interact with. In this sense social networks could enrich and simplify the communication between the user and technology avoiding the multiplication of custom interfaces. In this work the authors propose a system to enHancE human RobOt Interaction through common Social networks (HeROIS). HeROIS system combines the use of cloud resources, service robot and smart environments proposing three different services to help citizens in daily life. In order to assess the acceptability and the usability levels, HeROIS system and services have been tested with 13 real users (24–37 years old) in the DomoCasa Lab (Italy). As regards the usability, the results show that the proposed system is usable for 4 participants (30.77 % M = 79.69 SD = 3.13) and excellent for 9 participants (69.23 % M = 90.05 SD = 3.72). Concerning the acceptability level, the results show that the proposed system is acceptable for 8 volunteers (61.54 % M = 77.02 SD = 4.23) and excellent for 5 participants (38.46 % M = 89.71 SD = 6.06).

Keywords

Service robots Social network Cloud robotics Acceptability 

Notes

Acknowledgment

This work was supported in part by the European Community’s Seventh Framework Program (FP7/2007–2013) under grant agreement no. 288899 (Robot-Era Project). This work was also supported in part by Telecom Italia, Joint Open Lab WHITE, Pisa, Italy and OmniaRoboCare project - Programma Operativo Regionale CReO Fesr 2007–2013, Linea di intervento 1.5.a – 1.6, Bando unico R&S anno 2012.

References

  1. 1.
    Social Networking Fact Sheet (2014). http://www.pewinternet.org/fact-sheets/social-networking-fact-sheet/. Accessed 16 Oct 2014
  2. 2.
    BCS The chartered institute for IT (2013). http://www.bcs.org/content/conWebDoc/49824. Accessed 16 Oct 2014
  3. 3.
    Our Mobile Planet, Think with Google (2014). http://think.withgoogle.com/mobileplanet/en/. Accessed 16 Oct 2014
  4. 4.
    Solis P., Carlaw, S.: Consumer and Personal Robotics. ABI Research (2013)Google Scholar
  5. 5.
    Moschetti, A., Fiorini, L., Aquilano, M., Cavallo, F., Dario, P.: Preliminary findings of the AALIANCE2 ambient assisted living roadmap. In: Longhi, S., Siciliano, P., Germani, M., Monteriù, A. (eds.) Ambient Assisted Living, pp. 335–342. Springer, Switzerland (2014)CrossRefGoogle Scholar
  6. 6.
    Broadbent, Elizabeth, Stafford, Rebecca, MacDonald, Bruce: Acceptance of healthcare robots for the older population: review and future directions. Int. J. Soc. Robot. 1(4), 319–330 (2009)CrossRefGoogle Scholar
  7. 7.
    Emeli V., Christensen, H.: Enhancing the robot service experience through social media. RO-Man, 2011 IEEE. IEEE (2011)Google Scholar
  8. 8.
    Ma, X., Yang, X., Zhao, S., Fu, C., Lan, Z., Pu, Y.: Robots in my contact list: using a social media platforms for human-robot interaction in domestic environment. In: APCHI 2012, pp. 133–140. ACM, New York (2012)Google Scholar
  9. 9.
    Bell, D. et al:. Microblogging as a mechanism for human–robot interaction. Knowledge-Based Systems (2014)Google Scholar
  10. 10.
    Emeli, V.: Robot learning through social media crowdsourcing. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2332–2337, 7–12 October 2012Google Scholar
  11. 11.
    Kuffner J.J.: Cloud-enabled robots. IEEE-RAS International Conference on Humanoid Robotics, Nashville, TN (2010)Google Scholar
  12. 12.
    Cavallo, F., et al.: Development of a socially believable multi-robot solution from town to home. Cogn. Comput. 6(4), 954–967 (2014)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Dillon, A.: User acceptance of information technology. In: Karwowski, W. (ed.) Encyclopaedia of human factors and ergonomics. Taylor & Francis, London (2001)Google Scholar
  14. 14.
    Davis, F.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)CrossRefGoogle Scholar
  15. 15.
    Ajzen. I., Fishbein, M.: Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs (1980)Google Scholar
  16. 16.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: towards a unified view. MIS Q. 27(3), 425–478 (2003)Google Scholar
  17. 17.
    Heerink, M., Kröse, B., Evers, V., Wielinga, B.: Measuring acceptance of an assistive social robot: a suggested toolkit. In: The 18th IEEE International Symposium on Robot and Human Interactive Communication Toyama, Japan, September 27–October 2 2009Google Scholar
  18. 18.
    Santos, J.R.A.: Cronbach’s alpha: a tool for assessing the reliability of scales. J. Ext. 37(2), 1–5 (1999)Google Scholar
  19. 19.
    McLellan, S., Muddimer, A., Peres, S.C.: The effect of experience on system usability scale ratings. J. Usability Stud. 7(2), 56–67 (2012)Google Scholar
  20. 20.
    Twitter APIs. https://dev.twitter.com/overview/documentation. Accessed 17 oct 2014
  21. 21.
    Google Calendar API V3. https://developers.google.com/google-apps/calendar/. Accessed 18 Feb 2014
  22. 22.
    KuKa youBot official website. http://www.youbot-store.com/. Accessed 17 Oct 2014
  23. 23.
    Quigley, M. et al.: ROS: an open-source robot operating system. ICRA workshop on open source software, 3(3.2), pp. 803–821 (2009)Google Scholar
  24. 24.
    Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. Robotics Automation Magazine. IEEE (1997)Google Scholar
  25. 25.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005). ISBN 0-262-20162-3Google Scholar
  26. 26.
    Alhmiedat, T.A., Yang, S.H.: A ZigBee based mobile tracking system through wireless sensor network. Int. J. Adv. Mechatron. Syst. 1(1), 63–70 (2008)CrossRefGoogle Scholar
  27. 27.
    Kehoe, B., Patil, S., Abbeel, P., Goldberg, K.: A survey of research on cloud robotics and automation. IEEE Trans. Autom. Sci. Eng. (T-ASE) 12(2), 1–12 (2015)CrossRefGoogle Scholar
  28. 28.
    Natural Language Toolkit. http://www.nltk.org. Accessed 17 Oct 2014
  29. 29.
    Pivato, P., Palopoli, L., Petri, D.: Accuracy of RSS-based centroid localization algorithms in an indoor environment. IEEE Trans. Instrum. Meas. 60(10), 3451–3460 (2011)CrossRefGoogle Scholar
  30. 30.
    Yufeng, Q.J., Jianhua, M.: Integration of range-based and range-free localiza-tion algorithms in wireless sensor networks for mobile clouds. In: IEEE International Conference on and IEEE Cyber, Physical and Social Computing (2013)Google Scholar
  31. 31.
    Nunnally, J.C., et al.: Psychometric Theory, vol. 226. McGraw-Hill, New York (1967)Google Scholar
  32. 32.
    Kline, P.: Handbook of Psychological Testing. Routledge, New York (2013)Google Scholar
  33. 33.
    Lougheed, E.: Frazzled by Facebook? An exploratory study of gender differences in social network communication among undergraduate men and women. College Student J. 46(1), 88–99 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Laura Fiorini
    • 1
  • Raffaele Limosani
    • 1
  • Raffaele Esposito
    • 1
  • Alessandro Manzi
    • 1
  • Alessandra Moschetti
    • 1
  • Manuele Bonaccorsi
    • 1
  • Filippo Cavallo
    • 1
  • Paolo Dario
    • 1
  1. 1.The BioRobotics InstituteScuola Superiore Sant’AnnaPisaItaly

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