Abstract
Acceptance and engagement of users towards social robots is the yardstick by which the success and efficacy of Human-Robot Interaction (HRI) is measured. Metrics such as acceptance and engagement are normally measured through subjective (such as interviews) or objective measures (such as non-verbal behaviour). In this study, we report on a methodologically novel process of monitoring non-verbal behaviour of humans in visual images with a social robot. We qualitatively code pictures of humans with the Nao robot as found on Instagram. We specifically coded the emotions of the users as depicted by their facial expressions as well as their social distance maintained towards the robot. Our results show that in general humans persist with an overall positive, enthusiastic and engaging behaviour towards the Nao. Moreover, females were found to be much more expressive in their responses towards the Nao as compared to males - a gender effect. We conclude with the implications of our results and possible avenues of future research on the topic of measuring human engagement with social robots through visual stimuli.
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References
Baiget, P., Gonzalez, J.: Observing human behavior in image sequences: the video-hermeneutics challenge. In: Computer Vision: Advances in Research & Development, CVCRD 2008 (2008)
Bakhshi, S., Shamma, D.A., Gilbert, E.: Faces engage us: photos with faces attract more likes and comments on instagram. In: Proceedings of the 32nd Annual ACM Conference on Human factors in Computing Systems, pp. 965–974. ACM (2014)
Basil, M.: Use of photography and video in observational research. Qual. Mark. Res. Int. J. 14(3), 246–257 (2011)
Bethel, C.L., Murphy, R.R.: Review of human studies methods in HRI and recommendations. Int. J. Soc. Robot. 2(4), 347–359 (2010)
Deshpande, R.: “Paradigms lost”: on theory and method in research in marketing. J. Mark. 47(4), 101–110 (1983)
Eresha, G., Haring, M., Endrass, B., Andre, E., Obaid, M.: Investigating the influence of culture on proxemic behaviors for humanoid robots. In: RO-MAN, 2013 IEEE, pp. 430–435. IEEE (2013)
Eyssel, F., Hegel, F.: (S) he’s got the look: gender stereotyping of robots. J. Appl. Soc. Psychol. 42(9), 2213–2230 (2012)
Eyssel, F., Kuchenbrandt, D., Bobinger, S., de Ruiter, L., Hegel, F.: ’If you sound like me, you must be more human’: on the interplay of robot and user features on human-robot acceptance and anthropomorphism. In: Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 125–126. ACM (2012)
Fiore, S.M., Wiltshire, T.J., Lobato, E.J., Jentsch, F.G., Huang, W.H., Axelrod, B.: Toward understanding social cues and signals in human-robot interaction: effects of robot gaze and proxemic behavior. Front. Psychol. 4, 1–15 (2013)
Fridin, M., Belokopytov, M.: Acceptance of socially assistive humanoid robot by preschool and elementary school teachers. Comput. Hum. Behav. 33, 23–31 (2014)
Friedman, B., Kahn Jr., P.H., Hagman, J.: Hardware companions?: what online Aibo discussion forums reveal about the human-robotic relationship. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 273–280. ACM (2003)
Goodrich, M.A., Schultz, A.C.: Human-robot interaction: a survey. Found. Trends Hum. Comput. Interact. 1(3), 203–275 (2007)
Han, J., Campbell, N., Jokinen, K., Wilcock, G.: Investigating the use of non-verbal cues in human-robot interaction with a nao robot. In: 2012 IEEE 3rd International Conference on Cognitive Infocommunications (CogInfoCom), pp. 679–683. IEEE (2012)
Heenan, B., Greenberg, S., Aghelmanesh, S., Sharlin, E.: Designing social greetings and proxemics in human robot interaction. Technical report, University of Calgary (2013)
Jacobsson, M.: Play, belief and stories about robots: a case study of a Pleo blogging community. In: The 18th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2009, pp. 232–237. IEEE (2009)
Kim, Y., Mutlu, B.: How social distance shapes human-robot interaction. Int. J. Hum Comput Stud. 72(12), 783–795 (2014)
Koay, K.L., Dautenhahn, K., Woods, S., Walters, M.L.: Empirical results from using a comfort level device in human-robot interaction studies. In: Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-robot Interaction, pp. 194–201. ACM (2006)
LaFrance, M., Hecht, M.A., Paluck, E.L.: The contingent smile: a meta-analysis of sex differences in smiling. Psychol. Bull. 129(2), 305 (2003)
Mavridis, N.: A review of verbal and non-verbal human-robot interactive communication. Robot. Auton. Syst. 63, 22–35 (2015)
Mubin, O., Khan, A., Obaid, M.: # naorobot: exploring nao discourse on twitter. In: Proceedings of the 28th Australian Conference on Computer-Human Interaction, pp. 155–159. ACM (2016)
Mumm, J., Mutlu, B.: Human-robot proxemics: Physical and psychological distancing in human-robot interaction. In: Proceedings of the 6th International Conference on Human-robot Interaction, HRI 2011, NY, USA, pp. 331–338 (2011). http://doi.acm.org/10.1145/1957656.1957786
Obaid, M., Mubin, O., Basedow, C.A., Ünlüer, A.A., Bergström, M.J., Fjeld, M.: A drone agent to support a clean environment. In: Proceedings of the 3rd International Conference on Human-Agent Interaction, pp. 55–61. ACM (2015)
Obaid, M., Sandoval, E.B., Złotowski, J., Moltchanova, E., Basedow, C.A., Bartneck, C.: Stop! that is close enough. How body postures influence human-robot proximity. In: The 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 354–361. IEEE (2016)
Pantic, M., Pentland, A., Nijholt, A., Huang, T.: Human computing and machine understanding of human behavior: a survey. In: Proceedings of the 8th International Conference on Multimodal Interfaces, pp. 239–248. ACM (2006)
Riley, R.G., Manias, E.: The uses of photography in clinical nursing practice and research: a literature review. J. Adv. Nurs. 48(4), 397–405 (2004)
Roto, V., Oulasvirta, A., Haikarainen, T., Kuorelahti, J., Lehmuskallio, H., Nyyssonen, T.: Examining mobile phone use in the wild with quasi-experimentation. Helsinky Institute for Information Technology (HIIT), Technical Report 1, 2004 (2004)
Sabanovic, S., Michalowski, M.P., Simmons, R.: Robots in the wild: observing human-robot social interaction outside the lab. In: 2006 9th IEEE International Workshop on Advanced Motion Control, pp. 596–601. IEEE (2006)
Takayama, L., Pantofaru, C.: Influences on proxemic behaviors in human-robot interaction. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 5495–5502. IEEE (2009)
Vis, F., Thelwall, M.: Researching Social Media. Sage Publ., London (2013)
Yang, Y., Baker, S., Kannan, A., Ramanan, D.: Recognizing proxemics in personal photos. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3522–3529. IEEE (2012)
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Mubin, O., Patel, H., Khan, A., Obaid, M. (2017). An Image Based Non-verbal Behaviour Analysis of HRI. In: Kheddar, A., et al. Social Robotics. ICSR 2017. Lecture Notes in Computer Science(), vol 10652. Springer, Cham. https://doi.org/10.1007/978-3-319-70022-9_3
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DOI: https://doi.org/10.1007/978-3-319-70022-9_3
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