International Journal of Social Robotics

, Volume 5, Issue 3, pp 367–378 | Cite as

Automated Proxemic Feature Extraction and Behavior Recognition: Applications in Human-Robot Interaction

  • Ross Mead
  • Amin Atrash
  • Maja J. Matarić


In this work, we discuss a set of feature representations for analyzing human spatial behavior (proxemics) motivated by metrics used in the social sciences. Specifically, we consider individual, physical, and psychophysical factors that contribute to social spacing. We demonstrate the feasibility of autonomous real-time annotation of these proxemic features during a social interaction between two people and a humanoid robot in the presence of a visual obstruction (a physical barrier). We then use two different feature representations—physical and psychophysical—to train Hidden Markov Models (HMMs) to recognize spatiotemporal behaviors that signify transitions into (initiation) and out of (termination) a social interaction. We demonstrate that the HMMs trained on psychophysical features, which encode the sensory experience of each interacting agent, outperform those trained on physical features, which only encode spatial relationships. These results suggest a more powerful representation of proxemic behavior with particular implications in autonomous socially interactive and socially assistive robotics.


Proxemics Spatial interaction Spatial dynamics Sociable spacing Social robot Human-robot interaction PrimeSensor Microsoft Kinect 



This work is supported in part by an NSF Graduate Research Fellowship, as well as ONR MURI N00014-09-1-1031 and NSF IIS-1208500, CNS-0709296, IIS-1117279, and IIS-0803565 grants. We thank Louis-Philippe Morency for his insights in integrating his head pose estimation system [37] and in the experimental design process, and Mark Bolas and Evan Suma for their assistance in using the PrimeSensor, and Edward Kaszubski for his help in integrating the proxemic feature extraction and behavior recognition systems into the Social Behavior Library.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Interaction LabUniversity of Southern CaliforniaLos AngelesUSA

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