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      Characterizing Movement Fluency in Musical Performance: Toward a Generic Measure for Technology Enhanced Learning

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          Abstract

          Virtuosity in music performance is often associated with fast, precise, and efficient sound-producing movements. The generation of such highly skilled movements involves complex joint and muscle control by the central nervous system, and depends on the ability to anticipate, segment, and coarticulate motor elements, all within the biomechanical constraints of the human body. When successful, such motor skill should lead to what we characterize as fluency in musical performance. Detecting typical features of fluency could be very useful for technology-enhanced learning systems, assisting and supporting students during their individual practice sessions by giving feedback and helping them to adopt sustainable movement patterns. In this study, we propose to assess fluency in musical performance as the ability to smoothly and efficiently coordinate while accurately performing slow, transitionary, and rapid movements. To this end, the movements of three cello players and three drummers at different levels of skill were recorded with an optical motion capture system, while a wireless electromyography (EMG) system recorded the corresponding muscle activity from relevant landmarks. We analyzed the kinematic and coarticulation characteristics of these recordings separately and then propose a combined model of fluency in musical performance predicting music sophistication. Results suggest that expert performers' movements are characterized by consistently smooth strokes and scaling of muscle phasic coactivation. The explored model of fluency as a function of movement smoothness and coarticulation patterns was shown to be limited by the sample size, but it serves as a proof of concept. Results from this study show the potential of a technology-enhanced objective measure of fluency in musical performance, which could lead to improved practices for aspiring musicians, instructors, and researchers.

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          Most cited references91

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          Computational mechanisms of sensorimotor control.

          In order to generate skilled and efficient actions, the motor system must find solutions to several problems inherent in sensorimotor control, including nonlinearity, nonstationarity, delays, redundancy, uncertainty, and noise. We review these problems and five computational mechanisms that the brain may use to limit their deleterious effects: optimal feedback control, impedance control, predictive control, Bayesian decision theory, and sensorimotor learning. Together, these computational mechanisms allow skilled and fluent sensorimotor behavior. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Movement smoothness changes during stroke recovery.

            Smoothness is characteristic of coordinated human movements, and stroke patients' movements seem to grow more smooth with recovery. We used a robotic therapy device to analyze five different measures of movement smoothness in the hemiparetic arm of 31 patients recovering from stroke. Four of the five metrics showed general increases in smoothness for the entire patient population. However, according to the fifth metric, the movements of patients with recent stroke grew less smooth over the course of therapy. This pattern was reproduced in a computer simulation of recovery based on submovement blending, suggesting that progressive blending of submovements underlies stroke recovery.
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              Evidence for a distributed hierarchy of action representation in the brain.

              Complex human behavior is organized around temporally distal outcomes. Behavioral studies based on tasks such as normal prehension, multi-step object use and imitation establish the existence of relative hierarchies of motor control. The retrieval errors in apraxia also support the notion of a hierarchical model for representing action in the brain. In this review, three functional brain imaging studies of action observation using the method of repetition suppression are used to identify a putative neural architecture that supports action understanding at the level of kinematics, object centered goals and ultimately, motor outcomes. These results, based on observation, may match a similar functional-anatomic hierarchy for action planning and execution. If this is true, then the findings support a functional-anatomic model that is distributed across a set of interconnected brain areas that are differentially recruited for different aspects of goal-oriented behavior, rather than a homogeneous mirror neuron system for organizing and understanding all behavior.
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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                04 February 2019
                2019
                : 10
                : 84
                Affiliations
                [1] 1RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, Department of Musicology, University of Oslo , Oslo, Norway
                [2] 2Department of Architecture, Design and Media Technology, Aalborg University , Copenhagen, Denmark
                [3] 3Department of Music Education and Music Therapy, Norwegian Academy of Music , Oslo, Norway
                Author notes

                Edited by: Gualtiero Volpe, Università di Genova, Italy

                Reviewed by: Sergio Ivan Giraldo, Universidad Pompeu Fabra, Spain; Fabio Morreale, Queen Mary University of London, United Kingdom

                *Correspondence: Victor Gonzalez-Sanchez v.e.g.sanchez@ 123456imv.uio.no

                This article was submitted to Performance Science, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2019.00084
                6369163
                30778309
                cc776bab-1f3b-404e-83b4-adf9d004f135
                Copyright © 2019 Gonzalez-Sanchez, Dahl, Hatfield and Godøy.

                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.

                History
                : 09 July 2018
                : 11 January 2019
                Page count
                Figures: 14, Tables: 1, Equations: 1, References: 100, Pages: 20, Words: 14212
                Funding
                Funded by: Norges Forskningsråd 10.13039/501100005416
                Award ID: 262762
                Award ID: 250698
                Categories
                Psychology
                Technology Report

                Clinical Psychology & Psychiatry
                motor control,music performance,coarticulation,emg,motion capture,phase transition,smoothness

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