Principal components analysis to characterise fatigue-related changes in technique: Application to double under jump rope
Bibliographic record
Abstract
The upper extremities play an important role in managing the rope-turning technique required to perform continuous double unders. However, acute adaptions in this technique may occur as a jumper fatigues. The purpose of this study was to examine how turning technique is adapted with fatigue. Three-dimensional kinematic data of the upper extremity were collected from 10 trained athletes as they performed consecutive double unders to volitional fatigue. Time series wrist, elbow and shoulder joint angles were calculated where joint angle waveforms representing 10 unique trials from the beginning ("fresh") and end ("fatigued") of the continuous jumping protocol for all participants were analysed using principal component analysis. Participants reported stopping due to cardiovascular and shoulder muscular fatigue. From a kinematics perspective, with fatigue athletes used a more internally rotated range of motion at the shoulder, which we believe prompted a series of more distal adaptions in order to maintain rope turning, preserving consecutive double under performance. The presence of a maladaptive adaptation at the shoulder may increase the risk of developing shoulder injuries. Coaches should consider helping jumpers develop appropriate shoulder muscle endurance such that they can continue to maximise their training and proficiency, while protecting against potential fatigue-related maladaptation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".