1. Semiautomatic Behavioral Change-Point Detection: A Case Study Analyzing Children Interactions With a Social Agent
- Author
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Mohamed Chetouani, Liliana Lo Presti, Salvatore Maria Anzalone, Alain Berthoz, Mohamed Zaoui, David Cohen, Soizic Gauthier, Jean Xavier, Marco La Cascia, Vito Monteleone, Centre de Recherches sur la Cognition et l'Apprentissage (CeRCA), Centre National de la Recherche Scientifique (CNRS)-Université de Tours-Université de Poitiers, Sorbonne Université (SU), Perception, Interaction, Robotique sociales (PIROS), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Vito Monteleone, Liliana Lo Presti, Marco La Cascia, S. Gauthier, J. Xavier, M. Zaoui, A. Berthoz, D. Cohen, M. Chetouani, S. Anzalone, Université de Poitiers-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherches Psychanalyse, Médecine et Société (CRPMS (EA_3522)), Université Paris Diderot - Paris 7 (UPD7), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Collège de France (CdF (institution)), French National Centre for Scientific Research, Cognitions Humaine et ARTificielle (CHART), Université Paris 8 Vincennes-Saint-Denis (UP8)-École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Nanterre (UPN)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)
- Subjects
Computer science ,Semi-automated annotation ,Interpersonal communication ,Human Behavior ,Machine learning ,computer.software_genre ,Human behavior ,Task (project management) ,[SCCO]Cognitive science ,Annotation ,Artificial Intelligence ,Change-point ,Psychology ,Training ,ComputingMilieux_MISCELLANEOUS ,Psychiatry ,Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni ,business.industry ,Manual ,Computational modeling ,Social agents ,Dynamics (music) ,Task analysis ,Tool ,Task analysi ,Artificial intelligence ,business ,computer ,Software ,Change detection - Abstract
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 use-case 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.
- Published
- 2021