The PInSoRo dataset: Supporting the data-driven study of child-child and child-robot social dynamics

Séverin Lemaignan, Charlotte E. R. Edmunds, Emmanuel Senft, Tony Belpaeme, Michael L. Goodman
<span title="2018-10-19">2018</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
The study of the fine-grained social dynamics between children is a methodological challenge, yet a good understanding of how social interaction between children unfolds is important not only to Developmental and Social Psychology, but recently has become relevant to the neighbouring field of Human-Robot Interaction (HRI). Indeed, child-robot interactions are increasingly being explored in domains which require longer-term interactions, such as healthcare and education. For a robot to behave in
more &raquo; ... an appropriate manner over longer time scales, its behaviours have to be contingent and meaningful to the unfolding relationship. Recognising, interpreting and generating sustained and engaging social behaviours is as such an important -and essentially, open -research question. We believe that the recent progress of machine learning opens new opportunities in terms of both analysis and synthesis of complex social dynamics. To support these approaches, we introduce in this article a novel, open dataset of child social interactions, designed with data-driven research methodologies in mind. Our data acquisition methodology relies on an engaging, methodologically sound, but purposefully underspecified free-play interaction. By doing so, we capture a rich set of behavioural patterns occurring in natural social interactions between children. The resulting dataset, called the PInSoRo dataset, comprises 45+ hours of hand-coded recordings of social interactions between 45 child-child pairs and 30 child-robot pairs. In addition to annotations of social constructs, the dataset includes fully calibrated video recordings, 3D recordings of the faces, skeletal informations, full audio recordings, as well as game interactions. 100 Essential Social Interaction Predicates (ESIPs): rhythmic coupling (entrainment or 101 attunement), mimicry (behavioral matching), movement simultaneity, kinematic turn 102 taking patterns, joint attention. This dataset does not appear to be publicly available 103 on-line. 104 The UE-HRI dataset [11] is another recently published (2017) dataset of social 105 interactions, focusing solely on human-robot interactions. 54 adult participants were 106 recorded (duration M=7.7min) during spontaneous dialogues with a Pepper robot. The 107
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