Quantifying Asymmetry and Performance of Lower Limb Mechanical Muscle Function in Varsity Athletes—Using Non–Countermovement Jumps
Bibliographic record
Abstract
ABSTRACT: Stadnyk, M, Sepehri, M, Cook, M, Adeeb, S, and Westover, L. Quantifying asymmetry and performance of lower limb mechanical muscle function in varsity athletes-using non-countermovement jumps. J Strength Cond Res 37(1): 98-106, 2023-The ability to automatically quantify jump performance and lower limb muscle function in athletes would be beneficial for both training and rehabilitation purposes. Countermovement jumps (CMJs) and non-CMJs (NCMJs) are simple, quick, and require relatively inexpensive equipment to effectively and reliably monitor lower limb function. In a previous study, CMJ characteristics were assessed across different varsity sports. This study is a follow-up study to incorporate NCMJs into assessing jump characteristics of the same sports and investigate the additional information provided by the complementary jump type. The main objective of this study was to look at a means of quantifying the lower limb mechanical muscle function automatically to provide information for rehabilitation and performance purposes in athletes of specific sports. Male and female varsity athletes from 4 different sports completed 5 trials of an NCMJ on dual force plates. An analysis program was developed using Wolfram Mathematica to analyze force-time jump data. Various parameters of interest were generated, including peak force, force-time curve shape classification, jump phase lengths, phase-specific kinetic impulse, asymmetry index, takeoff velocity, jump height, phase-specific center of mass displacements, and reactive strength index modified. Results obtained indicate that similar jump characteristics to the CMJ study can be quantified, which can be used for performance enhancement or injury rehabilitation. Additional data found, such as the ability of an athlete to hold a steady squat during an NCMJ, could also be meaningful in aiding trainers to design programs tailored for athletes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".