Multimodal Detection of Engagement in Groups of Children Using Rank Learning

In collaborative play, children exhibit different levels of engagement. Some children are engaged with other children while some play alone. In this study, we investigated multimodal detection of individual levels of engagement using a ranking method and non-verbal features: turn-taking and body movement. Firstly, we automatically extracted turn-taking and body movement features in naturalistic and challenging settings. Secondly, we used an ordinal annotation scheme and employed a ranking method considering the great heterogeneity and temporal dynamics of engagement that exist in interactions. We showed that levels of engagement can be characterised by relative levels between children. In particular, a ranking method, Ranking SVM, outperformed a conventional method, SVM classification. While either turn-taking or body movement features alone did not achieve promising results, combining the two features yielded significant error reduction, showing their complementary power.