Towards a wearable monitoring tool for in‐field ice hockey skating performance analysis
Bibliographic record
Abstract
Abstract The capturing of movements by means of wearable sensors has become increasingly popular in order to obtain sport performance measures during training or competition. The purpose of the current study was to investigate the feasibility of using body worn accelerometers to identify previous highlighted performance related biomechanical changes in terms of substantial differences across skill levels and skating phases. Twenty‐two ice hockey players of different caliber were equipped with two 3D accelerometers, located on the skate and the waist, as they performed 30 m forward skating sprints on an ice rink. Two measures of the temporal stride characteristics (contact time and stride time) and one measure of the propulsive power (stride propulsion) of a skating stride were calculated and checked for discriminating effects across (i) skill levels and (ii) sprint phases as well as for their (iii) strength of association with the sprint performance (total sprint time). High caliber players showed an increased stride propulsion (+22%, P < 0.05) and shorter contact time (−5%, P < 0.05). All three analysed variables highlighted substantial biomechanical differences between the accelerative and constant velocity phases (P < 0.05). Stride propulsion of acceleration strides primarily correlated to total sprint time (r = −0.57, P < 0.05). The results demonstrate the potential of accelerometers to assess skating technique elements such as contact time or elements characterizing the propulsive power such as center of mass acceleration, to gauge skating performance. Thus, the findings of this study might contribute to establishing wearable sensors for in‐field ice hockey skating performance analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.002 |
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".