Cluster Analysis of Movement Patterns in Multiarticular Actions: A Tutorial

Rein, Robert; Button, Chris; Davids, Keith; Summers, Jeffery J.

doi:10.1123/mcj.14.2.211

Cited by 45 publications

(35 citation statements)

References 58 publications

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“…Standardized z-scores of the selected variables were calculated to compare data sets with different units and/or magnitudes. 14 ANOVA was used to identify the variables having the highest influence in each cluster, and discriminant analysis (stepwise method) was used to validate them (P ≤ .05). Total eta-squared (η 2 ) was selected as an effect-size index and interpreted as 31 without effect if 0 < η 2 ≤ .04, minimal if .04 < η 2 ≤ .25, moderate if .25 < η 2 ≤ .64, and strong if η 2 > .64.…”

Section: Discussionmentioning

confidence: 99%

“…Subjects grouped in a specific cluster share several common characteristics but are very dissimilar to others not belonging to that cluster. 14 This procedure has been mainly applied in scientific fields such as genetics, 15 motor control, 14,16 and psychology. 17 A few studies have been conducted on adult/elite swimmers to classify coordination patterns, 18 start patterns, 19 and race analysis.…”

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confidence: 99%

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Cluster Stability as a New Method to Assess Changes in Performance and its Determinant Factors Over a Season in Young Swimmers

Morais¹,

Silva²,

Marinho³

et al. 2015

International Journal of Sports Physiology and Performance

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Purpose: To apply a new method to identify, classify, and follow up young swimmers based on their performance and its determinant factors over a season and analyze the swimmers' stability over a competitive season with that method. Methods: Fifteen boys and 18 girls (11.8 ± 0.7 y) part of a national talent-identification scheme were evaluated at 3 different moments of a competitive season. Performance (ie, official 100-m freestyle race time), arm span, chest perimeter, stroke length, swimming velocity, speed fluctuation, coefficient of active drag, propelling efficiency, and stroke index were selected as variables. Hierarchical and k-means cluster analysis were computed. Results: Data suggested a 3-cluster solution, splitting the swimmers according to their performance in all 3 moments. Cluster 1 was related to better performances (talented swimmers), cluster 2 to poor performances (nonproficient swimmers), and cluster 3 to average performance (proficient swimmers) in all moments.Stepwise discriminant analysis revealed that 100%, 94%, and 85% of original groups were correctly classified for the 1st, 2nd, and 3rd evaluation moments, respectively (0.11 ≤ Λ ≤ 0.80; 5.64 ≤ χ 2 ≤ 63.40; 0.001 < P ≤ .68). Membership of clusters was moderately stable over the season (stability range 46.1-75% for the 2 clusters with most subjects). Conclusion: Cluster stability is a feasible, comprehensive, and informative method to gain insight into changes in performance and its determinant factors in young swimmers. Talented swimmers were characterized by anthropometrics and kinematic features. Keywords: prepubescent swimmers, seasonal adaptations, longitudinal assessment, classificationTwo of the most interesting research topics in the field of sports performance, and specifically in competitive swimming, are the identification of performance-determinant factors and performance modeling. Several research groups have focused on identifying the main performance determinants and how they interplay to improve performance. Performance of young swimmers is influenced by growth and maturation. 1 Biological maturation may promote changes in their biomechanics, motor control, and energetics, which may affect their expertise achievement. 2 Young swimmers experience different rates of development that progress according to their own time scale. 3 For example, 2 structural-equation models reported that anthropometrics influence swimmers' kinematics and hence their performance. 4,5 The second topic of research interest is to model performance over time. The model enables a researcher or sports analyst to predict a subject's performance at a given moment, for example, at a given age or competition (ie, mean stability, withinsubject analysis). 6 Longitudinal assessments can also be carried out to understand the relative changes of performance among the main athletes (ie, normative stability, between-subjects analysis). 1,7 New trends in sports performance and expertise should adopt a multidisciplinary approach to enhance our understanding of the a...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Cluster Stability as a New Method to Assess Changes in Performance and its Determinant Factors Over a Season in Young Swimmers

Morais¹,

Silva²,

Marinho³

et al. 2015

International Journal of Sports Physiology and Performance

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show abstract

“…The application of cluster analysis demands several processing steps [88,89]. First, the desired input variables have to be chosen, and if necessary time and/or range normalized.…”

Section: Cluster Analysismentioning

confidence: 99%

“…During each clustering step the correlation between the objects is calculated and the objects are grouped accordingly. This can be interpreted as sort of a hierarchical principal component analysis as the fundamental entity underlying PCA is the variance-covariance matrix [89].…”

Section: Cluster Analysismentioning

confidence: 99%

Measurement Methods to Analyze Changes in Coordination During Motor Learning from a Non-linear Perspective

Rein¹

2012

TOSSJ

Self Cite

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During the last two decades investigations into motor learning have gone beyond the traditional discrete summary statistics and more and more complex process oriented movement variables are being investigated. This increase in the complexity of data entails also an increase in the complexity of the data analysis. The present paper serves as an introduction for sports scientists to several different analysis methods, which have produced many interesting insights in the area of motor control and motor learning over the last few years, thereby highlighting non-linear aspects of motor learning. An approachable introduction to root-mean square measures, uncontrolled manifold analysis, principal component analysis, and cluster analysis is given. These analysis tools enable sports scientists to investigate motor learning from a non-linear perspective and to gain a better knowledge of the processes occurring during motor learning.

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“…An advantage is that the input variables can stem from many domains, and may include interval, ordinal, nominal or ratio-scaled scales (sometimes requiring prior normalization) (see Rein, Button, Davids, & Summers, 2010 for an in-depth methodological review). Interestingly for movement science, time-continuous data like kinetic movement patterns can also be clustered.…”

Section: Cluster Analysismentioning

confidence: 99%

What Variability tells us about motor expertise: measurements and perspectives from a complex system approach

Komar

Seifert

Thouvarecq

2015

Mov Sport Sci/Sci Mot

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Abstract. In this review, we revisit the concept of movement variability in motor expertise from the perspective of complex systems theory; we do so by defining performers and their relationships with the environment. The complex systems perspective places the focus on the functional role of movement variability as an emergent response to interacting constraints as performers pursue taskgoals. We therefore show how the shift in theoretical focus towards the functional role of variability has entailed a reconsideration of the way this key property of motor expertise should be assessed. We then present the main theoretical issues surrounding movement variability and expertise through examples of empirical applications of the relevant tools and concepts in the sport and movement sciences.Key words: Expert performance, adaptability, degeneracy, cluster analysis, recurrence quantification analysis Résumé. Ce que la variabilité motrice nous apprend sur l'expertise : mesures et perspectives du point de vue des théories de la complexité.À travers cette revue, nous revisitons le concept de variabilité motrice et sa relation à la notion d'expertise à partir d'un point de vue emprunté aux théories de la complexité. Pour cela, nous nous intéressons dans un premier temps à un niveau général qui décrit la relation qu'entretient un sujet avec son environnement. Au sein de cette relation sujet-environnement, une perspective issue des théories de la complexité porte alors une attention toute particulière au rôle fonctionnel de la variabilité motrice comme propriété émergente de l'interaction de contraintes pesant sur le sujet. À partir de ce postulat, nous proposons ensuite de reconsidérer la manière dont la variabilité motrice est évaluée à partir de l'utilisation de statistiques non-linéaires ou non-supervisés, ceci afin de répondre à cette acception de la variabilité comme élément clé de l'expertise motrice. Enfin, nous présentons quelques exemples de questionnements théoriques soulevés par notre positionnement à travers un approfondissement d'études empiriques réalisées dans le domaine du sport et des sciences du mouvement humain.

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Cluster Analysis of Movement Patterns in Multiarticular Actions: A Tutorial

Cited by 45 publications

References 58 publications

Cluster Stability as a New Method to Assess Changes in Performance and its Determinant Factors Over a Season in Young Swimmers

Cluster Stability as a New Method to Assess Changes in Performance and its Determinant Factors Over a Season in Young Swimmers

Measurement Methods to Analyze Changes in Coordination During Motor Learning from a Non-linear Perspective

What Variability tells us about motor expertise: measurements and perspectives from a complex system approach

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