@inproceedings{055a65f35ec448cd921691a7b638409d,
title = "Predicting Social Dynamics in Child-Robot Interactions with Facial Action Units",
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.",
keywords = "Human-Robot Interaction, Social Dynamics, Facial Action Coding System (FACS), Neural Network, Machine Learning, robot",
author = "{van Eijndhoven}, Kyana and Travis Wiltshire and Paul Vogt",
year = "2020",
month = mar,
day = "23",
doi = "10.1145/3371382.3378366",
language = "English",
series = "ACM/IEEE International Conference on Human-Robot Interaction",
publisher = "ACM, New York",
pages = "502--504",
booktitle = "HRI 2020 - Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction",
}