Are Junior Tennis Players Less Exposed to Shocks and Vibrations than Adults? A Pilot Study
Bibliographic record
Abstract
This study investigated muscle activation, shocks, and vibrations of the upper extremities during tennis serves between junior and adult tennis players. Thirty-five well-trained tennis players (15 juniors and 20 adults) performed 10 maximal successful tennis serves. Two triaxial accelerometers recorded the shock and vibration on the racket and the hand on the dominant side. Eight surface EMG electrodes were also used to measure the arm muscles' activities. Linear mixed models were used to test the fixed effect of age on muscular activation and vibration. Statistical non-Parametric Mapping was employed to make statistical inferences on the EMG and accelerometer data obtained from the continuous wavelet transform. Comparing EMG parameters between junior and adult players reveals similar upper limb intermuscular coordination. The junior players experienced lower racket and hand vibration amplitudes, which were partially explained by a lower ball velocity. This study revealed that young players showed no difference in EMG parameters in the tennis serve but were as exposed to shocks and vibrations as adults when compared based on a given speed and a given handgrip force. These vibrations apply to an immature skeleton, which can increase the risk of injuries caused by overuse. In addition, differences in the racket vibration frequency provide original knowledge to engineers who need to develop innovative sports equipment for tennis.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".