Gesture recognition technologies for gestural know-how management: Preservation and transmission of expert gestures in wheel throwing pottery

Abstract

The acquisition of gestural know-how in manual professions constitutes a real challenge since it passes from master to learner, through a many years long « in person » transmission. However this binding transmission is not always possible for practical reasons; the learner must train himself alone, by using traditional Knowledge Management tools such as e-documentation and multimedia contents. These tools present important limitations, only providing the learner expert knowledge in a descriptive way, with a low attractiveness and interaction level, without any sensorimotor feedback. It thus becomes crucial to find novel ways to preserve and transmit know-how. In this work we present the idea of a methodological framework for gestural know-how management in wheel throwing pottery, based on motion capture and gesture recognition technologies. In combination with machine learning techniques, they permit to model the practical, cinematic aspects of potter's expertise. These technologies can be used to compare experts' and learners' simulated performances and to provide real-time feedback to the learner, guiding him in the adjustment of his gestures. The final goal is to propose a novel and highly interactive embodied pedagogical application for gestural know-how transmission, supporting « self » trainings, and making them more efficient.