Automatic detection of skate strokes in short-track speed skating using one single IMU: validation of a new method
Bibliographic record
Abstract
Greater impulse is a key performance indicator of success in short track speed skating. The main objective of this study was to develop a method to measure skating strokes using a single IMU. Eight elite or world-class speed skaters had one IMU placed against their skin on the lower back, and a camera setup was positioned to capture the test. A maximal speed trial was then executed by each participant, and the data were analysed to estimate agreement between the camera and IMU estimates of skate stroke events. Inter-evaluator reliability was assessed on a dataset of 22 athletes performing speed trials as well. The algorithm detected 100% of the strokes identified on the video capture system with a root mean square error of 0.06s. Bland-Altman analysis showed a bias of 0.03s between the two methods, which corresponds to the frame rate of the camera. The inter-evaluator reliability yielded an intra-class correlation of 1.00 (ICC3,1) from a dataset of 7089 strokes. This study provides an example of on-ice evaluation of speed skating strokes using a single IMU. This equipment is less expensive than that employed by previous authors and can be implemented in training situations with low invasiveness.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".