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

Article

Abstract

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.

Keywords

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

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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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