Effects of Trunk Motion, Touch, and Articulation on Upper-Limb Velocities and on Joint Contribution to Endpoint Velocities During the Production of Loud Piano Tones

Verdugo, Felipe; Pelletier, Justine; Michaud, Benjamin; Traube, Caroline; Begon, Mickaël

doi:10.3389/fpsyg.2020.01159

Cited by 20 publications

(27 citation statements)

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“…Regardless of anthropometry, the trunk orients body position for all motor behaviors ( Magill & Anderson, 2017 ). During piano performance, changes in trunk angle (COG position) has a concomitant effect on arm movement ( Verdugo et al., 2020 ). Manipulation of trunk and arm angles determines hand-keyboard orientation.…”

Section: Discussionmentioning

confidence: 99%

“…For improving piano performance, most biomechanical and motor behavior research to date has employed protocols that emphasize reductionistic keystroke exercises ( Furuya & Kinoshita, 2008 ; Degrave et al., 2020 ; Oku & Furuya, 2017 ; Verdugo et al., 2020 ). Some have used scales, which are mechanical exercises designed to develop a pianist’s technique ( Ferrario et al., 2007 ; van Vugt et al., 2012 , 2014 ).…”

Section: Introductionmentioning

confidence: 99%

“…Given the significance of gross motor movement on fine motor execution and that pianists’ gross motor movements have been understudied, we aimed, in this study, to address anthropometry and musical context for pianists’ gross motor behaviors. One of the few studies in this realm analyzed trunk motion in pianists ( Verdugo et al., 2020 ), another analyzed hip kinetics (Massie-Laberge, Wanderley, et al., 2018), and several recognized core balance/center of gravity (COG) as an important factor in piano performance ( Koga & Nogami, 2012 ; Wristen, 2000 ; Zhang, 2020 ). Drawing on this preceding research, we examined two elite pianists’ motor behaviors during a complex musical performance so as to quantify pianists’ joint activity in the trunk, shoulders, elbows, and wrists and their dynamic balance shifts in COG.…”

Section: Introductionmentioning

confidence: 99%

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Pursuing Artful Movement Science in Music Performance: Single Subject Motor Analysis With Two Elite Pianists

et al. 2021

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Piano performance motor learning research requires more “artful” methodologies if it is to meaningfully address music performance as a corporeal art. To date, research has been sparse and it has typically constrained multiple performance variables in order to isolate specific phenomena. This approach has denied the fundamental ethos of music performance which, for elite performers, is an act of interpretation, not mere reproduction. Piano performances are intentionally manipulated for artistic expression. We documented motor movements in the complex task of performance of the first six measures of Chopin’s “Revolutionary” Etude by two anthropometrically different elite pianists. We then discussed their motor strategy selections as influenced by anthropometry and the composer’s musical directives. To quantify the joint angles of the trunk, shoulders, elbows, and wrists, we used a VICON 3 D motion capture system and biomechanical modeling. A Kistler force plate (1 N, Swiss) quantified center of gravity (COG) shifts. Changes in COG and trunk angles had considerable influence on the distal segments of the upper limbs. The shorter pianist used an anticipatory strategy, employing larger shifts in COG and trunk angles to produce dynamic stability as compensation for a smaller stature. Both pianists took advantage of low inertial left shoulder internal rotation and adduction to accommodate large leaps in the music. For the right arm, motor strategizing was confounded by rests in the music. These two cases illustrated, in principle, that expert pianists’ individualized motor behaviors can be explained as compensatory efforts to accommodate both musical goals and anthropometric constraints. Motor learning among piano students can benefit from systematic attention to motor strategies that consider both of these factors.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pursuing Artful Movement Science in Music Performance: Single Subject Motor Analysis With Two Elite Pianists

et al. 2021

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“…Previous studies of the comparison of keystrokes by professional pianists and novice pianists have shown that the control and organization of complex multi-joint motor action are influenced by long-term extensive training from an early age [74,[78][79][80][81][82][83]. Professional pianists work to reduce muscle load and achieve the physiological efficiency of fatigue-prone muscle by using more proximal joints and gravity, inter-segment, and reaction forces than novice piano players [43,84,85]. In addition, professional pianists have a smaller increase in co-activation of hand and finger muscles than amateurs and less stiffness with tempo, and the use of elbow motions allow fingertipkey contact with low stiffness across a wide range of tempi [74,86].…”

Section: Plos Onementioning

confidence: 99%

Quantitative analysis of piano performance proficiency focusing on difference between hands

et al. 2021

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Quantitative evaluation of piano performance is of interests in many fields, including music education and computational performance rendering. Previous studies utilized features extracted from audio or musical instrument digital interface (MIDI) files but did not address the difference between hands (DBH), which might be an important aspect of high-quality performance. Therefore, we investigated DBH as an important factor determining performance proficiency. To this end, 34 experts and 34 amateurs were recruited to play two excerpts on a Yamaha Disklavier. Each performance was recorded in MIDI, and handcrafted features were extracted separately for the right hand (RH) and left hand (LH). These were conventional MIDI features representing temporal and dynamic attributes of each note and computed as absolute values (e. g., MIDI velocity) or ratios between performance and corresponding scores (e. g., ratio of duration or inter-onset interval (IOI)). These note-based features were rearranged into additional features representing DBH by simple subtraction between features of both hands. Statistical analyses showed that DBH was more significant in experts than in amateurs across features. Regarding temporal features, experts pressed keys longer and faster with the RH than did amateurs. Regarding dynamic features, RH exhibited both greater values and a smoother change along melodic intonations in experts that in amateurs. Further experiments using principal component analysis (PCA) and support vector machine (SVM) verified that hand-difference features can successfully differentiate experts from amateurs according to performance proficiency. Moreover, existing note-based raw feature values (Basic features) and DBH features were tested repeatedly via 10-fold cross-validation, suggesting that adding DBH features to Basic features improved F1 scores to 93.6% (by 3.5%) over Basic features. Our results suggest that differently controlling both hands simultaneously is an important skill for pianists; therefore, DBH features should be considered in the quantitative evaluation of piano performance.

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“…biorbd was used in most of the project of the Laboratoire de Simulation et Modélisation du Mouvement (S2M); particularly in analysis settings (Desmyttere et al, 2020;Jackson et al, 2012;Verdugo et al, 2020) and simulation settings (Bélaise et al, 2018;Moissenet et al, 2019) for a wide variety of movements (walking, piano playing, upper limb maximal exertions, etc.) More recently, an optimal control framework for biomechanics (bioptim, based on Ipopt (Wächter & Biegler, 2006) and ACADOS (Verschueren et al, 2019) was developed around biorbd.…”

Section: Previous Usage Of Biorbdmentioning

confidence: 99%

biorbd: A C++, Python and MATLAB library to analyze and simulate the human body biomechanics

Michaud¹,

Begon²

2021

JOSS

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Biomechanics is at the interface of several fields of science, such as mechanics, human physiology and robotics. Although this transdisciplinarity encourages the emergence of new ideas, the variety of data to analyze simultaneously can be overwhelming. Commonly biomechanical datasets are composed of skin markers trajectories (termed as markers), contact forces, electromyography (EMG) signal, inertial measurement units (IMU) kinematics, etc., which by nature are not straightforward to combine. It is at their meeting point-the body movementthat biorbd steps in; bio standing for biomechanics and rbd for rigid body dynamics. biorbd is a feature-based development library that targets the manipulation of biomechanical data in a comprehensive and accessible manner. For a given musculoskeletal model, it provides functions for inverse flow-i.e.

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Effects of Trunk Motion, Touch, and Articulation on Upper-Limb Velocities and on Joint Contribution to Endpoint Velocities During the Production of Loud Piano Tones

Cited by 20 publications

References 58 publications

Pursuing Artful Movement Science in Music Performance: Single Subject Motor Analysis With Two Elite Pianists

Pursuing Artful Movement Science in Music Performance: Single Subject Motor Analysis With Two Elite Pianists

Quantitative analysis of piano performance proficiency focusing on difference between hands

biorbd: A C++, Python and MATLAB library to analyze and simulate the human body biomechanics

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