Recognition of spatial dynamics for predicting social interaction

Authors:
Ross Mead;Amin Atrash;Maja J. Mataric
Affiliations:
University of Southern California, Los Angeles, CA, USA;University of Southern California, Los Angeles, CA, USA;University of Southern California, Los Angeles, CA, USA
Venue:
Proceedings of the 6th international conference on Human-robot interaction
Year:
2011

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Abstract

We present a user study and dataset designed and collected to analyze how humans use space in face-to-face interactions. In a proof-of-concept investigation into human spatial dynamics, a Hidden Markov Model (HMM) was trained over a subset of features to recognize each of three interaction cues - initiation, acceptance, and termination - in both dyadic and triadic scenarios; these cues are useful in predicting transitions into, during, and out of multi-party social encounters. It is shown that the HMM approach performed twice as well as a weighted random classifier, supporting the feasibility of recognizing and predicting social behavior based on spatial features.