This paper identifies key properties of expertise in sport predicated on the performer-environment relationship. Weaknesses of traditional approaches to expert performance, which uniquely focus on the performer and the environment separately, are highlighted by an ecological dynamics perspective. Key properties of expert movement systems include 'multi- and meta-stability', 'adaptive variability', 'redundancy', 'degeneracy' and the 'attunement to affordances'. Empirical research on these expert system properties indicates that skill acquisition does not emerge from the internal representation of declarative and procedural knowledge, or the imitation of expert behaviours to linearly reduce a perceived 'gap' separating movements of beginners and a putative expert model. Rather, expert performance corresponds with the ongoing co-adaptation of an individual's behaviours to dynamically changing, interacting constraints, individually perceived and encountered. The functional role of adaptive movement variability is essential to expert performance in many different sports (involving individuals and teams; ball games and outdoor activities; land and aquatic environments). These key properties signify that, in sport performance, although basic movement patterns need to be acquired by developing athletes, there exists no ideal movement template towards which all learners should aspire, since relatively unique functional movement solutions emerge from the interaction of key constraints.

Key properties of expert movement systems in sport: An ecological dynamics perspective

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Perception Cognition And Decision Training The Quiet Eye In Action

The Test of Gross Motor Development 2 (TGMD-2) is currently the standard approach for assessing fundamental movement skills (FMS), including locomotor and object control skills. However, its extensive application is restricted by its low efficiency and requirement of expert training for large-scale evaluations. This study evaluated the accuracy of a newly-developed video-based classification system (VCS) with a marker-less sensor to assess children's locomotor skills. A total of 203 typically-developing children aged three to eight years executed six locomotor skills, following the TGMD-2 guidelines. A Kinect v2 sensor was used to capture their activities, and videos were recorded for further evaluation by a trained rater. A series of computational-kinematic-based algorithms was developed for instant performance rating. The VCS exhibited moderate-to-very good levels of agreement with the rater, ranging from 66.1% to 87.5%, for each skill, and 72.4% for descriptive ratings. Paired t-test revealed that there were no significant differences, but significant positive correlation, between the standard scores determined by the two approaches. Tukey mean difference plot suggested there was no bias, with a mean difference (SD) of -0.16 (1.8) and respective 95% confidence interval of 3.5. The kappa agreement for the descriptive ratings between the two approaches was found to be moderate (k = 0.54, p < 0.01). Overall, the results suggest the VCS could potentially be an alternative to the conventional TGMD-2 assessment approach for assessing children's locomotor skills without the necessity of the presence of an experienced rater for the administration.

https://www.jssm.org/hfpdf.php?volume=19&issue=3&page=585

A Video-Based Classification System for Assessing Locomotor Skills in Children

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Technological approaches to fundamental movement skill assessment in children

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Technological approaches to fundamental movement skill assessment in children