Biomechanical modeling as a practical tool for predicting injury risk related to repetitive muscle lengthening during learning and training of human complex motor skills
Bibliographic record
Abstract
Previous studies have shown that muscle repetitive stress injuries (RSIs) are often related to sport trainings among young participants. As such, understanding the mechanism of RSIs is essential for injury prevention. One potential means would be to identify muscles in risk by applying biomechanical modeling. By capturing 3D movements of four typical youth sports and building the biomechanical models, the current study has identified several risk factors related to the development of RSIs. The causal factors for RSIs are the muscle over-lengthening, the impact-like (speedy increase) eccentric tension in muscles, imbalance between agonists and antagonists, muscle loading frequency and muscle strength. In general, a large range of motion of joints would lead to over-lengthening of certain small muscles; Limb's acceleration during power generation could cause imbalance between agonists and antagonists; a quick deceleration of limbs during follow-throughs would induce an impact-like eccentric tension to muscles; and even at low speed, frequent muscle over-lengthening would cause a micro-trauma accumulation which could result in RSIs in long term. Based on the results, the following measures can be applied to reduce the risk of RSIs during learning/training in youth participants: (1) stretching training of muscles at risk in order to increase lengthening ability; (2) dynamic warming-up for minimizing possible imbalance between agonists and antagonists; (3) limiting practice times of the frequency and duration of movements requiring strength and/or large range of motion to reducing micro-trauma accumulation; and (4) allowing enough repair time for recovery from micro-traumas induced by training (individual training time). Collectively, the results show that biomechanical modeling is a practical tool for predicting injury risk and provides an effective way to establish an optimization strategy to counteract the factors leading to muscle repetitive stress injuries during motor skill learning and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".