The Proper Motor Control Model Revealed by Wheelchair Curling Quantification of Elite Athletes
Bibliographic record
Abstract
BACKGROUND: Wheelchair users are disadvantaged when it comes to accruing the benefits of physical activities. Hence, promoting various sports is crucial for keeping this population healthy. Since wheelchair curling can be played by individuals from a wide range of ages, strengths, and endurance levels, it has potential to improve wheelchair users' well-being. Yet, hardly any motion studies exist. This study aimed to facilitate understanding of optimized control of wheelchair curling for promoting wheelchair users' participation. METHODS: Using motion capture technology, nine national-level athletes were tested. Kinematic parameters related to segment/joint control and their coordination were quantified for both slow and fast curling. Descriptive statistics (means and standard deviations) and correlation analysis were applied for characterizing the skill. RESULTS: (1) Curling control consists of an acceleration phase and a stabilizing delivery phase; (2) the control of trunk, shoulder, and wrist are responsible for accelerating the rock; (3) elbow control is accountable for the accurate delivery of the rock; and (4) during the slow curling, a synchronized effort of trunk, shoulder, and wrist is used for accelerating the rock, while a sequential control among the segment/joints is applied in fast curling. CONCLUSIONS: The results supply valuable motor learning markers that could have a significant positive impact on the teaching and learning of wheelchair curling, as such, the findings have great potential for the health promotion of wheelchair users.
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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 | 0.000 |
| 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".