Biomechanical Insights Into Differences Between the Mid-Acceleration and Maximum Velocity Phases of Sprinting
Bibliographic record
Abstract
Yu, J, Sun, Y, Yang, C, Wang, D, Yin, K, Herzog, W, and Liu, Y. Biomechanical insights into differences between the mid-acceleration and maximum velocity phases of sprinting. J Strength Cond Res 30(7): 1906-1916, 2016-Investigating the differences between distinct phases of sprint running may increase the knowledge about the specific physical abilities needed for different phases of sprinting. Differences between the mid-acceleration and maximum velocity phases of sprint running have not yet been adequately investigated. Twenty male sprinters performed maximum-effort sprint runs, and measurements were made at 12 m from start for the mid-acceleration phase and at 40 m from the start for the maximum velocity phase. Kinematic data and ground reaction forces (GRFs) were collected at a rate of 200 and 1000 Hz, respectively. Intersegmental dynamics analysis was performed to investigate the interaction of muscle torque (MUS) with other passive torques. The peak horizontal braking force was significantly lower for the acceleration compared with that for the maximal velocity phase, whereas the peak horizontal propulsive force was similar for both phases. The peak MUS at the hip and knee joints for the braking phase was significantly smaller in the acceleration phase than in the maximum velocity phase. In conclusion, compared with the maximum velocity phase, the lower horizontal braking force was the primary cause for the increase in running velocity during the mid-acceleration phase. The force produced by lower limb muscles required to counteract external torques caused by the horizontal braking force in the braking phase was smaller during the acceleration phase than the maximum velocity phase. Therefore, training aimed at reducing the horizontal braking force might be more important than increasing the force produced by the lower limb muscles for success of the mid-acceleration phase.
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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) | 0.002 | 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".