Abstract
When willing to experience and learn a craft, “learning by doing” has been proven to be the most effective approach. Traditionally apprentices spend entire years close to the master, observing, imitating him/her, interacting with him/her, and receiving guidelines. Inspired by this natural process actively involving both the master and the apprentice, a technological metaphor has been developed permitting the user to discover and learn motor aspects of a craft. The user is invited to physically imitate and reproduce expert gestures and is guided then by an interactive mechanism on how to correct kinematic errors. This is achieved thanks to a Human-Centered AI Algorithm permitting to model of expert gestures/postures and then comparing them in real-time with the user’s ones to activate the sensorimotor feedback and to provide guidance for a better understanding of how a glass blower performs his/her expert gestures. This paper briefly presents the methodology followed for the development of this interactive mechanism and focuses on the results obtained from the experiments conducted. These results highlight that the feedback helps acquire glass blower motor skills, that different modalities are efficient for learning postures versus gestures, and that a retention effect is observed through three sessions of feedback use.
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Acknowledgement
This work was supported by the EU Horizon 2020 Innovation Program and Grant No. 822336, Mingei project. The authors would like to express their gratitude to Jean-Pierre Mateus from CERFAV for collaborating on the expert motion capture.
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Glushkova, A., Makrygiannis, D., Manitsaris, S. (2023). Interactive Sensorimotor Guidance for Learning Motor Skills of a Glass Blower. In: Rauterberg, M. (eds) Culture and Computing. HCII 2023. Lecture Notes in Computer Science, vol 14035. Springer, Cham. https://doi.org/10.1007/978-3-031-34732-0_3
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