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Record W7128226734 · doi:10.61801/oua.2024.2.21

BIOMECHANICAL CHARACTERISTICS OF THE UNDERSWING FROM THE LOWBAR TO THE HIGH BAR ON UNEVEN BARS

2024· article· W7128226734 on OpenAlexaboutno aff
Romania “Farul Constanta” Sports Club, Valerian Nicolae Forminte, Emilia Florina Grosu, Mirela Damian, Liliana Cosma, Vladimir Potop

Bibliographic record

VenueOvidius University Annals Series Physical Education and Sport Science movement and health · 2024
Typearticle
Language
FieldSocial Sciences
TopicPhysical Education and Gymnastics
Canadian institutionsnot available
Fundersnot available
KeywordsKinematicsBar (unit)Movement (music)TorsoAngular velocityCenter of gravityRepresentation (politics)Biomechanics

Abstract

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Aim. Identification and analysis of the kinematic and dynamic characteristics of the key elements of the underswing movement from the low bar to the high bar on uneven bars in women’s artistic gymnastics. Methods. This study aimed to optimize sports performances on uneven bars through a scientific approach. It involved seven gymnasts aged 12-15, who were members of Romanian national artistic gymnastics team. The analysis focused on exercises performed during the 2017 World Championships in Montreal. Various research methods were employed, including review of existing literature, use of video-computerized methods to analyze the techniques, employing the postural reference points and use of statistical and graphical representation methods. The study identified six key elements within the phasic structure of the underswing from the low bar (LB) to the high bar (HB) on uneven bars. These elements included preparatory movement phases (SF1.1, SF1.2, and SF2), basic movement phases, multiplication of body posture (MP) at maximum height of the center of gravity (GCG) or hip and concluding movement phases (PF1.1 and PF1.2). The angular features of body segments during the underswing were measured using Kinovea software, focusing on the angles between the hip and torso, and between the torso and arms. Anthropometric and biomechanical parameters necessary for the study were processed using the Physics ToolKit program. Kinematic characteristics such as segmental angular velocity and dynamic characteristics like the resultant force (N) were highlighted in the analysis. These findings provide valuable insights for optimizing gymnastic performances on uneven bars, enhancing technique and potentially improving competitive results. Results. Angular characteristics reveal the segmental angles of the body during various phases of the underswing from LB (low bar) to HB (high bar) on uneven bars. Analyzing these values provides a detailed picture of the positioning and evolution of the segmental angles. Anthropometric and biomechanical parameters that point out the basic moments of the underswing are presented. These parameters, including weight, height with arms stretched overhead, rotational inertia, and segmental movement radii highlight significant variations among athletes. An analysis of angular velocity and resultant force during the execution of the underswing from LB to HB on uneven bars offers a detailed perspective on the dynamics and effort involved in the different phases of the exercise. The results of the correlation analysis revealed the total number of links between the investigated indices, as well as their direction (negative and positive). The degree of connection varied from very weak correlations (42.87%) and weak ones (36.9%) to moderate (19.1%) and strong (1.2%) correlations. Variations occurred in the relations within movements phasic structure and in the specificities of each analyzed index. Conclusions. Using the video-computerized method in accordance with the method of postural reference points of the movement helped to identify and analyze the kinematic and dynamic characteristics of the key elements of the underswing from LB to HB on uneven bars. This fact can contribute to the improvement of sports performance on this apparatus. Keywords: women’s artistic gymnastics; key elements; angular features; angular velocity; resultant of force; performance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.334
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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