Predicting Social Dynamics in Child-Robot Interactions with Facial Action Units

Kyana van Eijndhoven*, Travis Wiltshire, Paul Vogt

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

We examine the extent to which task engagement, social engagement, and social attitude in child-robot interaction can be predicted on the basis of Facial Action Unit (FAU) intensity. The analyses were based on child-robot and child-child interaction data from the PInSoRo dataset [1]. We applied Logistic Regression, Naive Bayes, and Probabilistic Neural Networks to these data. Results indicated that FAU intensities have potential to predict social dynamics in child-robot interactions (average balanced accuracy scores up to 84%), and illustrate a difference in behavior of children towards other children when compared to their interaction with robots.
Original languageEnglish
Title of host publicationHRI 2020 - Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction
PublisherACM, New York
Pages502-504
Number of pages3
ISBN (Electronic)9781450370578
DOIs
Publication statusPublished - 23 Mar 2020

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
ISSN (Electronic)2167-2148

Keywords

  • Human-Robot Interaction
  • Social Dynamics
  • Facial Action Coding System (FACS)
  • Neural Network
  • Machine Learning
  • robot

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    van Eijndhoven, K., Wiltshire, T., & Vogt, P. (2020). Predicting Social Dynamics in Child-Robot Interactions with Facial Action Units. In HRI 2020 - Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (pp. 502-504). (ACM/IEEE International Conference on Human-Robot Interaction). ACM, New York. https://doi.org/10.1145/3371382.3378366