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Understanding Group Comfort Through Directional Statistics

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Social Robotics (ICSR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9388))

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Abstract

This paper has the dual aims of introducing and using directional statistics to investigate the comfort levels of pairs of people approached by a robot from different directions. Data from pairs seated in three maximally-different seating configurations are analysed. These data are in the form of circular distributions of ranked comfort levels. Statistical tests for uniformity of circular distributions and for determining if differences exist between pairs of circular distributions are introduced and used to analyse the directional properties of the data. It is shown that directional statistics can be used to compare comfort level ranks that capture all tested robot approach directions; something that cannot be achieved with linear statistics.

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Author information

Authors and Affiliations

  1. Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia

    Adrian Ball & David Rye

  2. Creative Robotics Lab, The University of New South Wales, Sydney, Australia

    David Silvera-Tawil & Mari Velonaki

Authors
  1. Adrian Ball
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  2. David Rye
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  3. David Silvera-Tawil
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  4. Mari Velonaki
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Corresponding author

Correspondence to Adrian Ball .

Editor information

Editors and Affiliations

  1. Robotics and Computer Vision Lab, ENSTA-ParisTech, Palaiseau, France

    Adriana Tapus

  2. Inst für Informatik, Human-Ctr Multimedi, Universität Augsburg, Augsburg, Bayern, Germany

    Elisabeth André

  3. LIMSI-CNRS, Orsay, France

    Jean-Claude Martin

  4. Robotics and Computer Vision Lab, ENSTA-ParisTech, Palaiseau, France

    François Ferland

  5. LIMSI-CNRS, Orsay, France

    Mehdi Ammi

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Ball, A., Rye, D., Silvera-Tawil, D., Velonaki, M. (2015). Understanding Group Comfort Through Directional Statistics. In: Tapus, A., André, E., Martin, JC., Ferland, F., Ammi, M. (eds) Social Robotics. ICSR 2015. Lecture Notes in Computer Science(), vol 9388. Springer, Cham. https://doi.org/10.1007/978-3-319-25554-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-25554-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25553-8

  • Online ISBN: 978-3-319-25554-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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