Movement interaction design for immersive media using interactive machine learning

Nicola Plant, Ruth Gibson, Carlos Gonzalez Diaz, Bruno Martelli, Michael Zbyszyński, Rebecca Fiebrink, Marco Gillies, Clarice Hilton, Phoenix Perry

    Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

    2 Citations (Scopus)


    Interactive Machine Learning is a promising approach for designing movement interaction because it allows developers to capture complex movements by simply performing them. We introduce a new tool being developed to make embodied interaction design faster, adaptable and accessible to developers of varying experience and background. Using the tool, we conduct workshops with creative practitioners and developers to explore techniques that equip users with embodied ideation design strategies encouraging full body interaction for immersive media.

    Original languageEnglish
    Title of host publicationProceedings of the 7th International Conference on Movement and Computing, MOCO 2020
    Publisher Association for Computing Machinery
    Number of pages2
    ISBN (Electronic)9781450375054
    ISBN (Print)9781450375054
    Publication statusPublished - 15 Jul 2020
    Event7th International Conference on Movement and Computing - Jersey City, Virtual, United States
    Duration: 15 Jul 202017 Jul 2020

    Publication series

    NameACM International Conference Proceeding Series


    Conference7th International Conference on Movement and Computing
    Abbreviated titleMOCO 2020
    Country/TerritoryUnited States
    CityJersey City, Virtual
    Internet address


    • immersive media
    • interaction design
    • machine learning
    • movement interaction
    • virtual reality

    ASJC Scopus subject areas

    • Human-Computer Interaction
    • Computer Networks and Communications
    • Computer Vision and Pattern Recognition
    • Software


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