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Influence of Environmental Context on Recognition Rates of Stylized Walking Sequences

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Book cover Social Robotics (ICSR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10652))

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Abstract

Affective movement will likely be an important component of robotic interaction as more and more robots move into human-facing scenarios where humans are (consciously or unconsciously) constantly monitoring the motion profile of counterparts in order to make judgments about the state of their counterpart. Many current studies in affective movement recognition and generation seek to either increase a machine’s ability to correctly identify human affect or to identify and create components of robotic movement that enhance human perception. However, very few of these studies investigate the influence of environmental context on a machine’s ability to correctly identity human affect or a human’s ability to correctly identify the affective intent of a robot. This paper presents the results of a user study that investigated how human perception of stylized walking sequences (created in [1]) varied based on the environment where they were portrayed. The results show that environment context can impact a person’s ability to correctly perceive the intended style of a movement.

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References

  1. Etemad, S.A., Arya, A.: Expert-driven perceptual features for modeling style and affect in human motion. IEEE Trans. Hum. Mach. Syst. 46(4), 534–545 (2016)

    Article  Google Scholar 

  2. Kleinsmith, A., Bianchi-Berthouze, N.: Affective body expression perception and recognition: a survey. IEEE Trans. Affect. Comput. 4(1), 15–33 (2013)

    Article  Google Scholar 

  3. Russell, J.A., Fehr, B.: Relativity in the perception of emotion in facial expressions. J. Exp. Psychol. Gen. 116(3), 223 (1987)

    Article  Google Scholar 

  4. Zacharatos, H., Gatzoulis, C., Chrysanthou, Y.L.: Automatic emotion recognition based on body movement analysis: a survey. IEEE Comput. Graph. Appl. 34(6), 35–45 (2014)

    Article  Google Scholar 

  5. Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S.: A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 39–58 (2009)

    Article  Google Scholar 

  6. Bernhardt, D., Robinson, P.: Detecting affect from non-stylised body motions. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds.) ACII 2007. LNCS, vol. 4738, pp. 59–70. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74889-2_6

    Chapter  Google Scholar 

  7. Bernhardt, D., Robinson, P.: Detecting emotions from connected action sequences. In: Badioze Zaman, H., Robinson, P., Petrou, M., Olivier, P., Schröder, H., Shih, T.K. (eds.) IVIC 2009. LNCS, vol. 5857, pp. 1–11. Springer, Heidelberg (2009). doi:10.1007/978-3-642-05036-7_1

    Chapter  Google Scholar 

  8. Etemad, S.A., Arya, A.: Modeling and transformation of 3D human motion. In: GRAPP, pp. 307–315 (2010)

    Google Scholar 

  9. Ribeiro, T., Paiva, A.: The illusion of robotic life: principles and practices of animation for robots. In: 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 383–390. IEEE (2012)

    Google Scholar 

  10. Van Breemen, A.: Bringing robots to life: applying principles of animation to robots. In: Proceedings of Shapping Human-Robot Interaction Workshop Held at CHI 2004, pp. 143–144 (2004)

    Google Scholar 

  11. LaViers, A., Egerstedt, M.: Controls and Art: Inquiries at the Intersection of the Subjective and the Objective. Springer, Cham (2014). doi:10.1007/978-3-319-03904-6_1

    Book  MATH  Google Scholar 

  12. Harmon-Jones, E., Gable, P.A., Price, T.F.: Does negative affect always narrow and positive affect always broaden the mind? Considering the influence of motivational intensity on cognitive scope. Curr. Dir. Psychol. Sci. 22(4), 301–307 (2013)

    Article  Google Scholar 

  13. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)

    Article  Google Scholar 

  14. Paltoglou, G., Thelwall, M.: Seeing stars of valence and arousal in blog posts. IEEE Trans. Affect. Comput. 4(1), 116–123 (2013)

    Article  Google Scholar 

  15. Kurdi, B., Lozano, S., Banaji, M.R.: Introducing the open affective standardized image set (OASIS). Behav. Res. Methods 49(2), 457–470 (2017)

    Article  Google Scholar 

  16. Dan-Glauser, E.S., Scherer, K.R.: The geneva affective picture database (GAPED): a new 730-picture database focusing on valence and normative significance. Behav. Res. Methods 43(2), 468 (2011)

    Article  Google Scholar 

  17. Barsade, S.G., Gibson, D.E.: Why does affect matter in organizations? Acad. Manag. Perspect. 21(1), 36–59 (2007)

    Article  Google Scholar 

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Acknowledgments

This work was supported by the Mechanical Science and Engineering Department and NSF grants #1701295 and #1528036.

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Authors and Affiliations

  1. Mechanical Science and Engineering Department, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA

    Madison Heimerdinger & Amy LaViers

Authors
  1. Madison Heimerdinger
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  2. Amy LaViers
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Corresponding authors

Correspondence to Madison Heimerdinger or Amy LaViers .

Editor information

Editors and Affiliations

  1. CNRS-AIST JRL, Tsukuba, Japan

    Abderrahmane Kheddar

  2. CNRS-AIST JRL, Tsukuba, Japan

    Eiichi Yoshida

  3. The National University of Singapore, Singapore, Singapore

    Shuzhi Sam Ge

  4. University of Tsukuba, Tsukuba, Japan

    Kenji Suzuki

  5. Qatar University, Doha, Qatar

    John-John Cabibihan

  6. CITEC, Bielefeld, Germany

    Friederike Eyssel

  7. Wichita State University, Wichita, Kansas, USA

    Hongsheng He

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Heimerdinger, M., LaViers, A. (2017). Influence of Environmental Context on Recognition Rates of Stylized Walking Sequences. 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_27

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  • DOI: https://doi.org/10.1007/978-3-319-70022-9_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70021-2

  • Online ISBN: 978-3-319-70022-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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