Improving the estimation of countermovement jump height from force plate recordings by considering the interaction between multiple procedural steps: An optimisation approach
Bibliographic record
Abstract
Force plates are used as standalone measurement systems in research and practice to evaluate metrics such as jump height. Calculating jump height involves multiple procedural steps, but previous investigations aiming to improve calculation procedures have only considered the influence of a single procedural step in isolation. The purpose of this study was to investigate if considering the interacting influence of multiple procedural steps in conjunction would impact the accuracy of jump height calculated from force plate recordings. An optimisation procedure was used to determine the combination of filter type, filter order, filter cut-off, integration start point and instant of take-off, that would minimize the root mean squared difference between force plate calculated jump height and a kinematic criterion. The best filter approach was a fifth order Butterworth filter with a 6 Hz cut-off frequency or a third order Chebyshev filter with a 5 Hz cut-off frequency. The best starting point for integration was approximately 0.25 s prior to the onset of the jump and the instant of take-off was best identified by finding the first instant that the force-time signal decreased by the magnitude of system weight. The presented optimisation technique provides an improved quantitative approach to develop standard procedures.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".