Automatic detection of children's engagement using non-verbal features and ordinal learning

In collaborative play, young children can exhibit different types of engagement. Some children are engaged with other children in the play activity while others are just looking. In this study, we investigated methods to automatically detect the children's levels of engagement in play settings using non-verbal vocal features. Rather than labelling the level of engagement in an absolute manner, as has frequently been done in previous related studies, we designed an annotation scheme that takes the order of children's engagement levels into account. Taking full advantage of the ordinal annotations, we explored the use of SVM-based ordinal learning, i.e. ordinal regression and ranking, and compared these to a rule-based ranking and a classification method. We found promising performances for the ordinal methods. Particularly, the ranking method demonstrated the most robust performance against the large variation of children and their interactions.