Reliability and validity of an indoor local positioning system for measuring external load in ice hockey players
Bibliographic record
Abstract
ABSTRACT This study determined the reliability and validity of a Kinexon local positioning system (LPS) for measuring external load in ice hockey players during an on‐ice session. Fourteen ice hockey players (25.1 y, 78.6 kg, 176.9 cm) wore two LPS sensors to examine the inter‐sensor reliability of the LPS during an on‐ice session, and LPS speed and acceleration were measured during 40 m linear on‐ice sprints and compared to a previously validated robotic sprint device to examine LPS accuracy. The coefficient of variation (CV), standard error of measurement (SEM), and intra‐class correlation coefficient (ICC) were calculated for each LPS measure. Pearson's correlations, simple linear regressions, and Bland–Altman plots were used to test the agreement and relationship between the two systems. Statistical significance was determined at p < 0.05. The majority of LPS measures were reliable (CV < 10% and ICC > 0.9) when comparing the two sensors worn by each player. Peak speed, speed at 5 m, and 0‐5 m acceleration were all comparable to those reported by the robotic sprint device, with nearly perfect (peak speed and 0–5 m acceleration) and very large (speed at 5 m) magnitudes of correlation and mean biases <0.5 km/hr for speed measures and <0.01 m/s2 for acceleration. The present results demonstrate that the Kinexon LPS is reliable and accurate for investigating on‐ice external load in ice hockey players when sensors are consistently secured on the back of the players’ shoulder pads.
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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 | 0.005 | 0.016 |
| 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 | 0.001 | 0.001 |
| 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".