Comunicació presentada a: 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education, celebrat el 13 de novembre de 2017 a Glasgow, Regne Unit.

We train and evaluate two machine learning models for predicting fingering in violin performances using motion and EMG sensors integrated in the Myo device. Our aim is twofold: first, provide a fingering recognition model in the context of a gamification virtual violin application where we measure both right hand (i.e. bow) and left hand (i.e. fingering) gestures, and second, implement a tracking system for a computer assisted pedagogical tool for self-regulated learners in high-level music education. Our approach is based on the principle of mapping-by-demonstration in which the model is trained by the performer. We evaluated a model based on Decision Trees and compared it with a Hidden Markovian Model.

This work has been partly sponsored by the Spanish TIN project TIMUL (TIN 2013-48152-C2-2-R), the European Union Horizon 2020 research and innovation programme under grant agreement No. 688269 (TELMI project), and the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).