Effect of whole-body vibration on lower-limb EMG activity in subjects with and without spinal cord injury
Bibliographic record
Abstract
OBJECTIVE: Traumatic spinal cord injury (SCI) results in substantial reductions in lower extremity muscle mass and bone mineral density below the level of the lesion. Whole-body vibration (WBV) has been proposed as a means of counteracting or treating musculoskeletal degradation after chronic motor complete SCI. To ascertain how WBV might be used to augment muscle and bone mass, we investigated whether WBV could evoke lower extremity electromyography (EMG) activity in able-bodied individuals and individuals with SCI, and which vibration parameters produced the largest magnitude of effect. METHODS: Ten male subjects participated in the study, six able-bodied and four with chronic SCI. Two different manufacturers' vibration platforms (WAVE(®) and Juvent™) were evaluated. The effects of vibration amplitude (0.2, 0.6 or 1.2 mm), vibration frequency (25, 35, or 45 Hz), and subject posture (knee angle of 140°, 160°, or 180°) on lower extremity EMG activation were determined (not all combinations of parameters were possible on both platforms). A novel signal processing technique was proposed to estimate the power of the EMG waveform while minimizing interference and artifacts from the plate vibration. RESULTS: WBV can elicit EMG activity among subjects with chronic SCI, if appropriate vibration parameters are employed. The amplitude of vibration had the greatest influence on EMG activation, while the frequency of vibration had lesser but statistically significant impact on the measured lower extremity EMG activity. CONCLUSION: These findings suggest that WBV with appropriate parameters may constitute a promising intervention to treat musculoskeletal degradation after chronic SCI.
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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) | 0.002 | 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".