Vertical and Fore-Aft Seat-to-Head Transmissibility Response to Vertical Whole Body Vibration: Gender and Anthropometric Effects
Bibliographic record
Abstract
In this study, the biodynamic responses to vertical vibration are investigated in terms of seat-to-head vibration transmissibility (STHT) to study the effects of gender and eleven different anthropometric parameters on the STHT responses in the vertical and fore-aft directions. The STHT response of 31 male and 27 female human subjects were measured under three levels of vertical vibration (0.25, 0.50 and 0.75 m/s2 rms acceleration) in the 0.50 to 20 Hz range, while seated without a back support and against a vertical back support with hands on a steering wheel. The results showed that the vertical and fore-aft STHT responses of the two genders were distinctly different. The primary resonance frequency of the male subjects was higher than the female subjects, while the peak magnitudes were comparable. The male subjects showed relatively greater softening effect, i.e. decrease in the primary resonance frequency with increase in excitation magnitude, as compared to the female subjects, irrespective of the sitting condition. The body mass revealed strong effect on both the male and female STHT responses. The primary resonance frequency of heavier subjects was lower than that of the lighter subjects, while the peak magnitude was higher for the heavier subjects. The male subjects showed significantly higher primary resonance frequency than the female subjects, even when comparable body mass, BMI and lean body mass were considered. The vertical STHT response of the two genders with same body fat mass was very similar for the sitting and excitation conditions considered in the study, particularly up to 10 Hz.
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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) | 0.003 | 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".