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 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 | 0.000 | 0.000 |
| 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".