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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 paper proposes a methodology to semi-automatically 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 paper describes the necessary steps to detect and classify change-points in behavioral data by using a dataset collected in a real usecase scenario. This dataset includes motor observations from children with typical development and with neuro-developmental disorders. Abnormal movements which 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 semi-automated annotation task; can be efficient in presence of abnormal behaviors; may achieve good performance when trained with limited manually annotated data.

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