Investigating the agreement between force platform and plantar pressure insole data in barefoot and skating-specific footwear conditions across four different movement patterns
Bibliographic record
Abstract
Conducting applied sport research in real-world settings is challenged by the lack of portable instrumentation capable of producing valid and reliable data that is collected without interfering with the athletes’ movement and is meaningful to sport performance. The purpose of this study was to compare and contrast data collected simultaneously from a force platform and a plantar pressure insole to provide support for the use of the XSENSOR® plantar pressure insoles in a skating-performance application. Data was collected in two conditions. The barefoot condition consisted of the insole on a force platform in isolation and the in-skate condition consisted of the insole inside a speed skate boot on a force platform. A single-participant design was conducted whereby an injury-free female completed multiple trials in both conditions. A P6000 Force Platform (BTS Bioengineering Corp., MI, Italy) was used to collect force data and an X4 Foot and Gait Measurement Systems plantar pressure insole (XSENSOR® Technology Corporation, AB, Canada) was used to collect pressure data in the two conditions across four movement patterns; static stance (SS), anterior-posterior sway (AP), medial-lateral sway (ML), and lateral jump (LJ). Intraclass correlation coefficients (ICC) and Bland-Altman plots revealed excellent agreement between the cumulative centre of pressure path length (mm) measured from the force platform and insole in the barefoot condition and moderate to excellent agreement between impulse (Ns) measured from the force platform and insole, in both conditions. These outcomes provide researchers and practitioners with empirical support and confidence to employ the XSENSOR® plantar pressure insoles outside of a laboratory setting in a real-world skating environment to collect and analyse in-skate kinetic data during on ice performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".