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

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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.

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Notes

  1. http://www.xbox.com/kinect.

  2. http://www.asus.com/Multimedia/Motion_Sensor.

  3. http://www.primesense.com.

  4. These proxemic distances pertain to Western American culture—they are not cross-cultural.

  5. In this implementation, head pose was used to estimate the visual code; however, as the size of each person’s face in the recorded image frames was rather small, the results from the head tracker were quite noisy [37]. If the head pose estimation confidence was below some threshold, the system would instead rely on the shoulder pose for eye gaze estimates.

  6. In this implementation, we utilized the 3-dimensional point cloud provided by our motion capture system for improved accuracy (see Sect. 4.1); however, we assume nothing about the implementations of others, so total distance can be used for approximations in the general case.

  7. More formally, Hall’s touch code distinguishes between caressing and holding, feeling or caressing, extended or prolonged holding, holding, spot touching (hand peck), and accidental touching (brushing); however, automatic extraction of such forms of touching go beyond the scope of this work.

  8. http://openni.org.

  9. http://www.pointclouds.org/documentation/tutorials/planar_segmentation.php.

  10. http://www.ros.org/wiki/openni_kinect/kinect_accuracy.

  11. For example, we do not measure the radiant heat or odor transmitted by one individual and the intensity at the corresponding sensory organ of the receiving individual.

  12. This occurs for the SFP axis estimates, as well as at the public distance interval, the loud and very loud voice loudness intervals, and the outside reach kinesthetic interval.

  13. https://code.google.com/p/usc-interaction-software.ros.

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Acknowledgements

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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  1. Interaction Lab, University of Southern California, Los Angeles, USA

    Ross Mead, Amin Atrash & Maja J. Matarić

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Correspondence to Ross Mead.

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Mead, R., Atrash, A. & Matarić, M.J. Automated Proxemic Feature Extraction and Behavior Recognition: Applications in Human-Robot Interaction. Int J of Soc Robotics 5, 367–378 (2013). https://doi.org/10.1007/s12369-013-0189-8

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