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<item>
  <id>05987305</id>
  <dt>a</dt>
  <an>05987305</an>
  <augroup>
    <au>Mead, Ross</au>
    <au>Atrash, Amin</au>
    <au>Matari\'c, Maja J.</au>
  </augroup>
  <ti>Proxemic feature recognition for interactive robots: automating metrics from the social sciences.</ti>
  <so>Mutlu, Bilge (ed.) et al., Social robotics. Third international conference, ICSR 2011, Amsterdam, The Netherlands, November 24--25, 2011. Proceedings. Berlin: Springer (ISBN 978-3-642-25503-8/pbk). Lecture Notes in Computer Science 7072. Lecture Notes in Artificial Intelligence, 52-61 (2011).</so>
  <py>2011</py>
  <pu>Berlin: Springer</pu>
  <lagroup>
    <la>EN</la>
  </lagroup>
  <ccgroup>
  </ccgroup>
  <utgroup>
    <ut>proxemics</ut>
    <ut>spatial interaction</ut>
    <ut>spatial dynamics</ut>
    <ut>social spacing</ut>
    <ut>social robot</ut>
    <ut>human-robot interaction</ut>
    <ut>PrimeSensor</ut>
    <ut>Microsoft Kinect</ut>
  </utgroup>
  <cigroup>
  </cigroup>
  <ligroup>
    <li>doi:10.1007/978-3-642-25504-5_6</li>
  </ligroup>
  <abgroup>
    <ab>Summary: In this work, we discuss a set of metrics for analyzing human spatial behavior (proxemics) motivated by work in the social sciences. Specifically, we investigate individual, attentional, interpersonal, and physiological factors that contribute to social spacing. We demonstrate the feasibility of autonomous real-time annotation of these spatial features during multi-person social encounters. We utilize sensor suites that are non-invasive to participants, are readily deployable in a variety of environments (ranging from an instrumented workspace to a mobile robot platform), and do not interfere with the social interaction itself. Finally, we provide a discussion of the impact of these metrics and their utility in autonomous socially interactive systems.</ab>
    <rv></rv>
  </abgroup>
</item>