Motor variability regulation analysis in trampolinists
Bibliographic record
Abstract
In trampolining, optimizing body orientation during landing reduces injury risk and enhances performance. As trampolinists are subject to motor variability, anticipatory inflight corrections are necessary to regulate their body orientation before landing. We investigated the evolution of a) body orientation and b) limb position (i.e., arms and legs) variabilities. Secondary objectives were to investigate c) the link between acrobatics difficulty and the variability accumulation, and d) to identify links between body orientation variability and gaze orientation. Kinematics and gaze orientation were captured using inertial measurement units and an eye tracker, respectively. Seventeen trampolinists performed up to 13 different acrobatics (different number of rotations in twist and somersault). Intra-trampolinist pelvis orientation and limb position inter-trial variability was computed for each acrobatic at three key timestamps: takeoff, 75 % completion of the twist, and landing. Pelvis orientation variability significantly increased between takeoff and the instant when 75 % of the twist is completed (+75 %) and then decreased from the instant when 75 % of the twist is completed until landing (-39 %). Conversely, limb variability decreased (upper limbs: -66 % and lower limbs: -46 %), before increasing (+357 % and +127 %), suggesting that trampolinists adapted their limb kinematics to regulate pelvis orientation before landing. It was qualitatively observed that this decrease in body orientation variability occurred mostly when trampolinists were looking at the trampoline bed before landing. In addition, there was a moderate correlation between the number of twists in a straight somersault and the variability accumulation at 75 % of the twist, highlighting that trampolinists accumulate more variability as the number of twist rotations increases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".