A kinematic analysis of high-speed treadmill sprinting over a range of velocities
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to measure changes in stride characteristics and lower-extremity kinematics of the hip and knee as a function of increasing treadmill velocity, at velocities ranging from submaximal to near maximal. METHODS: Six power/speed athletes experienced at sprinting on a treadmill performed trials at 70%, 80%, 90%, and 95% of their previous individual maximum velocity, with video data collected in the sagittal view at 60 Hz. RESULTS: Significant differences were seen in stride frequency (70%, 80%, P < 0.01; 90%, P < 0.05), stance time (70%, 80%, P < 0.01; 90%, P < 0.05) flight time (70%, P < 0.01; 80%, P < 0.05), hip flexion angle (70%, P < 0.01), hip flexion angular velocity (70%, P < 0.01), hip extension angular velocity (70%, 80%, P < 0.01), knee flexion angular velocity (70%, 80%, P < 0.01), and knee extension angular velocity (70%, P < 0.01), as compared with the near maximum (95%) velocity. Coefficient of variation (CV) values showed that the positional variables at the hip and knee were more variable at faster test conditions, indicating that kinematic changes occur as a function of increased treadmill velocity. CONCLUSIONS: The results indicated that at slower velocities, there were differences in the stride characteristics and lower-extremity kinematics while sprinting on a treadmill. As the velocity approached near maximum mechanical breakdown was seen, suggesting that velocities greater than 90% should be used selectively during treadmill training.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".