Semiautomatic Behavioral Change-Point Detection: A Case Study Analyzing Children Interactions with a Social Agent

The study of human behaviors in cognitive sciences provides clues to understand and describe people's personal and interpersonal functioning. In particular, the temporal analysis of behavioral dynamics can be a powerful tool to reveal events, correlations, and causalities but also to discover abnormal behaviors. However, the annotation of these dynamics can be expensive in terms of temporal and human resources. To tackle this challenge, this article proposes a methodology to semiautomatically annotate behavioral data. Behavioral dynamics can be expressed as sequences of simple dynamical processes: transitions between such processes are generally known as change points. This article describes the necessary steps to detect and classify change points in behavioral data by using a data set collected in a real use-case scenario. This data set includes motor observations from children with typical development and with neurodevelopmental disorders. Abnormal movements that are present in such disorders are useful to validate the system in conditions that are challenging even for experienced annotators. Results show that the system: can be effective in the semiautomated annotation task; can be efficient in presence of abnormal behaviors; may achieve good performance when trained with limited manually annotated data.