Emergence of single and double peaks in individual force-time curves of the counter-movement jump (CMJ)
Bibliographic record
Abstract
The force-time curves of countermovement jumps (CMJ) are often analyzed in jump diagnostics in order to draw conclusions about the quality of the jump. A distinction is often made between one- and two-peaked (also called “unimodal” and “bimodal”) curves, but there is little research on the angular movements in the lower body that cause them. To fill this research gap, the present study recorded three different variants of the CMJ in two subjects using both a force plate and a motion capture system (n = 12 jumps). It could be shown that the two peaks resulted firstly from the fact that ankle plantar flexion started later than hip and knee extension and secondly that hips and knees were accelerated less at the beginning of the upward movement and more strongly in the further course until take-off. Regarding the jump variants, the greatest jump heights were obtained when the jumpers either chose their individual execution or tried to complete the jump as smooth as possible, which both produced two-peaked curves. If the countermovement was performed as quickly as possible, one-peaked force-time curves were generated, and lightly smaller jump heights were achieved. The theoretical considerations that single-peaked curves stand for an optimal intersegmental coordination and therefore should lead to better jump heights are contradicted by the empirical findings. The study contributed to explaining how two-peaked curves emerge. This should be of some importance for both researchers and coaches for this diagnostic jump, which is very relevant in many sports.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".