Automatic analysis of infant engagement during play: An end-to-end learning and Explainable AI pilot experiment

Infant engagement during play is an active area of research, related to the development of cognition. Automatic detection of engagement could benefit the research process, but existing techniques used for automatic affect detection are unsuitable for this scenario, since they rely on the automatic extraction of facial and postural features trained on clear video capture of adults. This study shows that end-to-end Deep Learning methods can successfully detect engagement of infants, without the need of clear facial video, when trained for a specific interaction task. It further shows that attention mapping techniques can provide explainability, thereby enabling trust and insight into a model’s reasoning process.