This evaluation study explores how automated movement analysis can be used to catch the biomechanical skills needed for a physically accurate violin performance, maximizing efficiency and minimizing injuries. Starting from a previously recorded multimodal dataset, we compute movement features from motion captured data of five violinists performing three violin exercises: octave shift, string crossing, and a Romantic repertoire piece. Three violin teachers were asked to evaluate audio, video, and both audio and video stimuli of the selected exercises. We correlated their ratings with automatically extracted movement features. Whereas these features are purely visual (i.e., they are computed from motion captured data only), we asked teachers to also evaluate audio because it can be considered as the direct translation of movement skills into another modality. In this way, we can also look at possible relations between evaluation of the audio aspects of the performance and biomechanical skills of violin playing. Results show that the proposed movement features can be partially used to measure the biomechanical skills of the violin players to support learning and mitigate the risk of injuries.

Automatically measuring biomechanical skills of violin performance: An exploratory study / Volta, Erica; Mancini, Maurizio; Varni, Giovanna; Volpe, Gualtiero. - (2018), pp. 1-4. (Intervento presentato al convegno 5th International Conference on Movement and Computing, MOCO 2018 tenutosi a Casa Paganini - InfoMus International Research Centre of DIBRIS - University of Genoa, ita) [10.1145/3212721.3212840].

Automatically measuring biomechanical skills of violin performance: An exploratory study

Mancini, Maurizio;
2018

Abstract

This evaluation study explores how automated movement analysis can be used to catch the biomechanical skills needed for a physically accurate violin performance, maximizing efficiency and minimizing injuries. Starting from a previously recorded multimodal dataset, we compute movement features from motion captured data of five violinists performing three violin exercises: octave shift, string crossing, and a Romantic repertoire piece. Three violin teachers were asked to evaluate audio, video, and both audio and video stimuli of the selected exercises. We correlated their ratings with automatically extracted movement features. Whereas these features are purely visual (i.e., they are computed from motion captured data only), we asked teachers to also evaluate audio because it can be considered as the direct translation of movement skills into another modality. In this way, we can also look at possible relations between evaluation of the audio aspects of the performance and biomechanical skills of violin playing. Results show that the proposed movement features can be partially used to measure the biomechanical skills of the violin players to support learning and mitigate the risk of injuries.
2018
5th International Conference on Movement and Computing, MOCO 2018
3-D motion analysis; Biomechanical modelling; Motor control; Motor modelling; Movement analysis; Multimodal interactive systems; Music learning; Music performance; Violin pedagogy; Human-Computer Interaction; Computer Networks and Communications; Software
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Automatically measuring biomechanical skills of violin performance: An exploratory study / Volta, Erica; Mancini, Maurizio; Varni, Giovanna; Volpe, Gualtiero. - (2018), pp. 1-4. (Intervento presentato al convegno 5th International Conference on Movement and Computing, MOCO 2018 tenutosi a Casa Paganini - InfoMus International Research Centre of DIBRIS - University of Genoa, ita) [10.1145/3212721.3212840].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1528182
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