Technical performance analyses in elite Paralympic swimming using wearable technology: two case studies
Bibliographic record
Abstract
We present two case studies that make use of wearable technology to provide performance indicators on optimal swim stroke techniques in breaststroke (case 1) and freestyle (case 2). In the first case study, we present and use a novel metric, the velocity variation score, to maximise breaststroke technical performance for an athlete with Achondroplasia Dwarfism, by comparing their normal technique to two alternates, focused on 1) fast arm sculling and 2) high stroke rate (HSR). We observed lower velocity variation scores using the adapted breaststroke techniques (p < 0.001), the HSR technique had the lowest velocity variation score (p < 0.001). In the second case, we determine the optimal breathing strategy, breathing to the impaired or unimpaired side, for an athlete with a unilateral hand impairment performing freestyle swimming. Results showed that the forward velocity was significantly lower in the left-to-right stroke cycle transition and right (arm pull) when breathing to the impaired (left) side. To varying degrees, these cases demonstrate that wearable-based intra-stroke analyses can provide individualised technique recommendations that benefit competitive race peformance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".