Relationship Between Lower Limb Muscle Activity and Platform Acceleration During Whole-Body Vibration Exercise
Bibliographic record
Abstract
The purpose of this study was to identify the influence of different magnitudes and directions of the vibration platform acceleration on surface electromyography (sEMG) during whole-body vibration (WBV) exercises. Therefore, a WBV platform was used that delivers vertical vibrations by a side-alternating mode, horizontal vibrations by a circular mode, and vibrations in all 3 planes by a dual mode. Surface electromyography signals of selected lower limb muscles were measured in 30 individuals while they performed a static squat on a vibration platform. The WBV trials included 2 side-alternating trials (Side-L: 6 Hz, 2.5 mm; Side-H: 16 Hz, 4 mm), 2 circular trials (Circ-L: 14 Hz, 0.8 mm; Circ-H: 43 Hz, 0.8 mm), and 4 dual-mode trials that were the combinations of the single-mode trials (Side-L/Circ-L, Side-L/Circ-H, Side-H/Circ-L, Side-H/Circ-H). Furthermore, control trials without vibration were assessed, and 3-dimensional platform acceleration was quantified during the vibration. Significant increases in the root mean square of the sEMG (sEMGRMS) compared with the control trial were found in most muscles for Side-L/Circ-H (+17 to +63%, p ≤ 0.05), Side-H/Circ-L (+7 to +227%, p ≤ 0.05), and Side-H/Circ-H (+21 to +207%, p < 0.01) and in the lower leg muscles for Side-H (+35 to +138%, p ≤ 0.05). Furthermore, only the vertical platform acceleration showed a linear relationship (r = 0.970, p < 0.001) with the averaged sEMGRMS of the lower limb muscles. Significant increases in sEMGRMS were found with a vertical acceleration threshold of 18 m·s(-2) and higher. The present results emphasize that WBV exercises should be performed on a platform that induces vertical accelerations of 18 m·s(-2) and higher.
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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 | 0.000 | 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) | 0.001 | 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".