WhoLoDancE: Whole-body Interaction Learning for Dance Education

Anna Rizzo, Katerina El Raheb, Sarah Whatley, Rosemary E. Kostic Cisneros, Massimiliano Zanoni, Antonio Camurri, Vladimir Viro, Jean-marc Matos, Stefano Piana, Michele Buccoli, Amalia Markatzi, Pablo Palacio, Oshri Even-Zohar, Augusto Sarti, Yannis Ioannidis, Edwin Morley-Fletcher

    Research output: Contribution to journalConference articlepeer-review

    20 Citations (Scopus)
    552 Downloads (Pure)


    Dance resides among the most ancestral forms of art, representing a major asset of the human intangible cultural heritage playing, at the same time, a primary role in contemporary artistic creation. WhoLoDancE, a Research and Innovation Action funded under the European Union’s Horizon 2020 research and innovation programme, aimed at the double goal of preserving its inheritance and integrating digital technologies into contemporary dance learning, teaching and choreography through the digitalisation of dance movements with motion capture techniques, the creation of a large motion repository - including movements from ballet, contemporary, flamenco and traditional Greek folk dances - and the implementation of breakthrough applications ranging from movement quality annotation and segmentation, similarity search, movement blending, multimodal and virtual reality-based experiences for self-reflection and experimentation. In this paper, we present the prototype tools and state-of-the-art results of the project development in its conclusive phase, highlighting the added value this interdisciplinary approach could possibly bring to dance learning and practice, the main technical, practical and cultural challenges encountered in this path and open issues to be addressed in the months to come, providing hints on future research directions.
    Original languageEnglish
    Pages (from-to)41-50
    Number of pages10
    JournalCEUR Workshop Proceedings
    Publication statusPublished - 3 Nov 2018


    • Dance
    • Education
    • Virtual Reality
    • Digital Technology
    • Intangible Cultural Heritage
    • Machine Learning
    • Information Technology
    • Motion capture
    • Learning


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