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CalliSmart: An Adaptive Informed Environment for Intelligent Calligraphy Training

Authors:
Rémy Frenoy
Indira Thouvenin
Yann Soullard
Olivier Gapenne

Keywords: Training Systems, Interactive systems, Adaptive Systems, Gesture Recognition

Abstract:
Gesture learning is a complex and multistep process where trainees are supposed to improve several psychomotor and cognitive skills. According to numerous studies, trainees need to be provided with various types of feedback to improve these skills. These studies also highlight that benefits of a given type of feedback depend on trainees situation. Therefore, feedback must be chosen according to an analysis of trainees activity. Sensorimotor approaches have investigated the impact of feedback on specific learning situations, but the analysis of gestural activity, which would allow the automatic selection of an appropriate type of feedback, is still a recurring issue. In this paper, we propose a new model for gestural training systems based on smart interaction. This model relies on a recognition module based on Naive Bayes classifiers, representing trainees activity by a vector describing their errors, and representing training environments by vectors describing their set of implemented types of feedback. We present a platform for calligraphy training we designed and developed based on our model. Through a user study, we emphasize the benefits of our approach on trainees development.

Pages: 132 to 137

Copyright: Copyright (c) IARIA, 2016

Publication date: April 24, 2016

Published in: conference

ISSN: 2308-4138

ISBN: 978-1-61208-468-8

Location: Venice, Italy

Dates: from April 24, 2016 to April 28, 2016