Transmission of Whole-Body Vibration and Its Effect on Muscle Activation
Bibliographic record
Abstract
The aim of current study was to measure the transmission of whole-body vibration through the entire body and to relate this to body posture and induced muscular activation. Eight clinically healthy subjects performed 3 static body postures-high squat (135°), deep squat (110°), and erect stance, whereas vibration transmission was assessed over a wide range of accelerations (from 0.33 to 7.98 g) and frequencies (from 30 to 50 Hz). To assess the vibration transmission, 9 triaxial accelerometers were attached from the ankle up to the head and the root mean square of acceleration signal of each site-specific body point was calculated. Additionally, muscle activity from 7 lower limb muscles was recorded. The results showed a significant attenuation of the platform accelerations transmitted from the feet to the head. Compared with erect stance, knee bent posture significantly diminished vibration transmission at the hip, spine, and the head (p < 0.05). Vibration transmission to the spine was significantly lower in deep vs. high squat (p < 0.05), suggesting that further knee bending may reduce the risk of overloading the spine. Vibration increased the muscle activity in most leg and hip muscles during both squat postures, although, on average, no clear dose-response relationship between the acceleration and/or frequency and muscle response was found. The muscular activation of vastus medialis and rectus femoris showed clear negative correlation to the vibration transmission at the sternum. The specific vibration parameters used in the present study can be considered as safe and suitable for a training program. Moreover, the present results contribute to optimize the most advantageous whole-body vibration protocol and to determine the beneficial effects on muscle and bone.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".