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Recognition of Group Actions from Individual Actions in Brainstorming Sessions

Author

Listed:
  • Shigeru Fujita

    (CIT - Chiba Institute of Technology)

  • Kenji Sugawara

    (CIT - Chiba Institute of Technology)

  • Thierry Gidel

    (COSTECH - Connaissance Organisation et Systèmes TECHniques - UTC - Université de Technologie de Compiègne)

  • Claude Moulin

    (Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne] - UTC - Université de Technologie de Compiègne - CNRS - Centre National de la Recherche Scientifique)

  • Insaf Setitra

    (Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne] - UTC - Université de Technologie de Compiègne - CNRS - Centre National de la Recherche Scientifique)

Abstract

In this paper we show how an artificial intelligence system is able to recognize actions of a group of people during remote meetings. It is based on the combination of several basic detections. We explain why it is a very important task in order to help a coach manage teams. In particular, we explain how the recognition of discussions allows to measure the collaboration between participants or possibly their isolation. We detail the action of Eye Contact where two people look at each other during a discussion. We present the algorithm of the module of our system in charge of the recognition of an Eye Contact action and the difficulties raised by this module. We conclude with the heuristics rules that would allow to detect higher level of group actions from individual actions in brainstorming sessions.

Suggested Citation

  • Shigeru Fujita & Kenji Sugawara & Thierry Gidel & Claude Moulin & Insaf Setitra, 2025. "Recognition of Group Actions from Individual Actions in Brainstorming Sessions," Post-Print hal-05388863, HAL.
    • Handle: RePEc:hal:journl:hal-05388863
    Note: View the original document on HAL open archive server: https://hal.science/hal-05388863v1
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    References listed on IDEAS

    as
    1. Eunji Chong & Elysha Clark-Whitney & Audrey Southerland & Elizabeth Stubbs & Chanel Miller & Eliana L. Ajodan & Melanie R. Silverman & Catherine Lord & Agata Rozga & Rebecca M. Jones & James M. Rehg, 2020. "Detection of eye contact with deep neural networks is as accurate as human experts," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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