Influence of Support Conditions on Vertical Whole-body Vibration of the Seated Human Body
Bibliographic record
Abstract
The vibration transmission to the lumbar and thoracic segments of seated human subjects exposed to whole body vibration of a vehicular nature have been mostly characterised without the back and hand supports, which is not representative of general driving conditions. This non-invasive experimental study investigated the transmission of vertical seat vibration to selected vertebrae and the head along the vertical and fore-aft axes of twelve male human subjects seated on a rigid seat and exposed to random vertical excitation in the 0.5-20 Hz range. The measurements were performed under four different sitting postures involving combinations of back support conditions and hands positions, and three difference magnitudes of vertical vibration (0.25, 0.5 and 1.0 m/s(2) rms acceleration). The results showed significant errors induced by sensor misalignment and skin effects, which required appropriate correction methodologies. The averaged corrected responses revealed that the back support attenuates vibration in the vertical axis to all the body locations while increasing the fore-aft transmissibility at the C7 and T5. The hands position generally has a relatively smaller effect, showing some influences on the C7 and L5 vibration. Sitting without a back support resulted in very low magnitude fore-aft vibration at T5, which was substantially higher with a back support, suggestive of a probable change in the body's vibration mode. The effect of back support was observed to be very small on the horizontal vibration of the lower thoracic and lumbar regions. The results suggest that distinctly different target body-segment biodynamic functions need to be defined for different support conditions in order to represent the unique contribution of the specific support condition. These datasets may then be useful for the development of biodynamic models.
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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".