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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".