Automatic Event Detection Using Wearable Technology During Short-Track Speed Skating Races
Bibliographic record
Abstract
The performance in short-track speed skating (STSS) is driven by technique optimization. However, because of discomfort, clutter, or complexity, regular instrumentation may not be suitable for use in daily training. The objective of this study was to validate a single-accelerometer-based algorithm: 1) to detect the number of strokes and 2) accurately classify left, right, pivot, and straight-line strokes during four- and nine-lap practice race simulations. Twenty-eight athletes from the Canadian National STSS team were instrumented with an accelerometer taped to their sacrums that would collect tridimensional accelerations and angles from start to finish, and they were filmed with a single camera setup during four-lap and/or nine-lap individual race trials. Data were analyzed with a custom MATLAB algorithm and compared to video data on two datasets to investigate the number of strokes, pivots, and straights detected. Over 98% of strokes were detected; and over 99% right/left strokes, 97.7% pivots, and 98.6% straights were identified. The validation led to intraclass correlation coefficients [ICC(3, 1)] of over 0.97, indicating an excellent agreement between the two methods. The results support the ability of wearable technology to deliver valid speed-skating data, enabling rapid feedback to coaches and athletes with minimal equipment in 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".