Comparison of Multiple Hop Test Kinematics Between Force-Platforms and Video Footage – A Cross Sectional Study
Bibliographic record
Abstract
Background: Multiple hop performances have been assessed using force-platforms and motion-capture cameras. However, the accessibility of these technologies might be a hindering factor for many performance coaches. Currently, tablet devices are being used as alternatives to measure jumping and hopping performances. Objective: This study aimed to compare multiple hop kinematics using the Kinovea application with force-platforms, the gold standard. Methods: Using an observational cross-sectional study design, male athletes (n=44; age 20.1 ± 1.4 years) completed triple hops (3-Hop) and quintuple hops (5-Hop) on force-platforms while being filmed using an iPad. Ground contact time, flight time and total time were analysed using Kinovea and compared with the force platform data. Results: Statistical analysis showed a high level of agreement across all variables of interest but significant differences (flight time; -2.14 to -5.96 %, ground contact time; 4.89 to 5.83 %, total time; -0.37 to -0.58%) were observed across all variables of interest. A systematic bias for flight and ground contact times were seen for 3-Hop and 5-Hop. Conclusion: The use of iPad and Kinovea application can be used as a valid alternative to measure multiple hop kinematics when performance coaches do not have access to expensive force-platforms or motion-capture cameras.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".