Insights from a Nine-Segment Biomechanical Model and Its Simulation for Anthropometrical Influence on Individualized Planche Learning and Training in Gymnastics
Bibliographic record
Abstract
The Planche is a challenging, the most required, and a highly valued gymnastic skill. Yet, it is understudied biomechanically. This article aims to explore the anthropometric variations that could affect the quality of balancing control in the Planche and to identify the body types that have an advantage in learning and training. To achieve this goal, a 9-segment rigid-body model is designed to simulate the skill performance by using 80 different body types. The results demonstrate that body type is a critical factor in determining an individual's innate ability to perform the Planche. The innate ability is affected by body mass, height, gender, and race. The findings reveal that a personalized training plan based on an individual's body type is necessary for optimal learning and training. A one-size-fits-all approach may not be effective since each individual's body type varies. Additionally, this study emphasizes the importance of considering segmental and/or limb characteristics in designing effective training plans. This study concludes that, for a given height, individuals with relatively longer legs and a shorter trunk (the characteristics of Europeans in comparison to Asians) could be better suited to perform the Planche. This suggests that European body types are naturally more advanced than Asian body types when it comes to performing the Planche. The practical implications of the current study are that practitioners can use biomechanical modeling and simulation techniques to identify body types that are most suited for the Planche and design training programs that are tailored to individual body types for optimizing their learning and training.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".