Biodynamic Response and Spinal Load Estimation of Seated Body in Vibration Using Finite Element Modeling
Bibliographic record
Abstract
Trunk biomechanical models play an indispensable role in predicting muscle forces and spinal loads under whole-body vibration (WBV) exposures. Earlier measurements on the force-motion biodynamic response (impedance, apparent mass) at the body-seat interface and vibration transmissibility (seat to head) have led to the development of different mechanical models. Such models could simulate the overall passive response and serve as an important tool for vehicle seat design. They cannot, however, evaluate physiological parameters of interest under the WBV. On the contrary, anatomical models simulating human's physiological characteristics can predict activities in muscles and their dynamic effects on the spine. In this study, a kinematics-driven nonlinear finite element model of the spine, in which the kinematics data are prescribed, is used to analyse the trunk response in seated WBV. Predictions of the active model (i.e., with varying muscle forces) as compared with the passive model (i.e., with no muscle forces) compared satisfactorily with measurements on vertical apparent mass and seat-to-head transmissibility biodynamic responses. Results demonstrated the crucial role of muscle forces in the dynamic response of the trunk. Muscle forces, while maintaining trunk equilibrium, substantially increased the compression and shear forces on the spine and, hence, the risk of tissue injury.
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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".