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Computational Approaches to Music Motor Performance: Clustering of Percussion Kinematics Underlying Performance Style

The present study investigated motor kinematics underlying performance-related movements in marimba performance. Participants played a marimba while motion capture equipment tracked movements of the torso, shoulders, elbows, wrists, and hands. Principal components analysis was applied to assess the...

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Autores principales: Loria, Tristan, Huang, Aiyun, Henechowicz, Tara Lynn, Thaut, Michael H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8716460/
https://www.ncbi.nlm.nih.gov/pubmed/34975617
http://dx.doi.org/10.3389/fpsyg.2021.725016
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author Loria, Tristan
Huang, Aiyun
Henechowicz, Tara Lynn
Thaut, Michael H.
author_facet Loria, Tristan
Huang, Aiyun
Henechowicz, Tara Lynn
Thaut, Michael H.
author_sort Loria, Tristan
collection PubMed
description The present study investigated motor kinematics underlying performance-related movements in marimba performance. Participants played a marimba while motion capture equipment tracked movements of the torso, shoulders, elbows, wrists, and hands. Principal components analysis was applied to assess the movements during the performance related to sound production and sound preparation. Subsequent cluster analyses sought to identify coupling of limb segment movements that may best characterize performance styles present in the performance. The analysis revealed four clusters that were thought to reflect performance styles of expressive performance, postural sway, energy efficiency, and a blend of the former styles. More specifically, the expressive cluster was best characterized by limb movements occurring along the vertical z-axis, whereas the postural sway cluster was characterized by forwards and backwards motions of the torso and upper limbs. The energy efficient cluster was characterized by movements of the body moving left to right along the marimba, whereas the blended style demonstrated limited delineation from the alternate styles. Such findings were interpreted as evidence that performance styles occur within a framework of biomechanical constraints and hierarchical stylistic factors. Overall, the results provided a more holistic understanding of motor execution in percussion performance.
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spelling pubmed-87164602021-12-31 Computational Approaches to Music Motor Performance: Clustering of Percussion Kinematics Underlying Performance Style Loria, Tristan Huang, Aiyun Henechowicz, Tara Lynn Thaut, Michael H. Front Psychol Psychology The present study investigated motor kinematics underlying performance-related movements in marimba performance. Participants played a marimba while motion capture equipment tracked movements of the torso, shoulders, elbows, wrists, and hands. Principal components analysis was applied to assess the movements during the performance related to sound production and sound preparation. Subsequent cluster analyses sought to identify coupling of limb segment movements that may best characterize performance styles present in the performance. The analysis revealed four clusters that were thought to reflect performance styles of expressive performance, postural sway, energy efficiency, and a blend of the former styles. More specifically, the expressive cluster was best characterized by limb movements occurring along the vertical z-axis, whereas the postural sway cluster was characterized by forwards and backwards motions of the torso and upper limbs. The energy efficient cluster was characterized by movements of the body moving left to right along the marimba, whereas the blended style demonstrated limited delineation from the alternate styles. Such findings were interpreted as evidence that performance styles occur within a framework of biomechanical constraints and hierarchical stylistic factors. Overall, the results provided a more holistic understanding of motor execution in percussion performance. Frontiers Media S.A. 2021-12-16 /pmc/articles/PMC8716460/ /pubmed/34975617 http://dx.doi.org/10.3389/fpsyg.2021.725016 Text en Copyright © 2021 Loria, Huang, Henechowicz and Thaut. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Loria, Tristan
Huang, Aiyun
Henechowicz, Tara Lynn
Thaut, Michael H.
Computational Approaches to Music Motor Performance: Clustering of Percussion Kinematics Underlying Performance Style
title Computational Approaches to Music Motor Performance: Clustering of Percussion Kinematics Underlying Performance Style
title_full Computational Approaches to Music Motor Performance: Clustering of Percussion Kinematics Underlying Performance Style
title_fullStr Computational Approaches to Music Motor Performance: Clustering of Percussion Kinematics Underlying Performance Style
title_full_unstemmed Computational Approaches to Music Motor Performance: Clustering of Percussion Kinematics Underlying Performance Style
title_short Computational Approaches to Music Motor Performance: Clustering of Percussion Kinematics Underlying Performance Style
title_sort computational approaches to music motor performance: clustering of percussion kinematics underlying performance style
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8716460/
https://www.ncbi.nlm.nih.gov/pubmed/34975617
http://dx.doi.org/10.3389/fpsyg.2021.725016
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