Moving Toward Individual-Specific Automotive Seat Design: How Individual Characteristics and Time Alter the Selected Lumbar Support Prominence
Bibliographic record
Abstract
OBJECTIVE: To explore how individual characteristics influence selected lumbar support prominence (LSP), seated lumbar flexion, seatback average pressure, contact area, and center of pressure (CoP) location before and after 1 hr of driving. BACKGROUND: An LSP can alter posture and may reduce low back pain during prolonged driving. Although LSP preference varies across individuals and may change over time, few investigations have explored the time-varying response to individually selected adjustable seat parameters. METHOD: Forty individuals selected LSP settings in an automotive seat through a series of systematic adjustment trials. The average LSP setting was fixed for a 1-hr driving simulation, followed by one final adjustment trial. Regressions were performed between individual characteristics and selected LSP, lumbar posture, and measures of seatback pressure from the initial adjustment trials. ANOVAs were performed to determine the effect of time and sex on these dependent variables. Discomfort was also monitored throughout the protocol. RESULTS: Individual's standing lumbar lordosis, selected LSP, and height and mass were significant predictors for seated lumbar flexion, seatback average pressure, and contact area, respectively. Discomfort levels remained low; however, following the driving protocol, individuals altered their posture to decrease lumbar flexion and increase seatback average pressure without significant adjustments to the LSP. CONCLUSION: These findings highlight individual characteristics to consider in automotive seat design and that the method for determining LSP settings may facilitate appropriate LSP selection. APPLICATION: A systematic method to determine LSP settings may reduce discomfort and automate seat adjustments, such that only short-term postural adjustments may be required.
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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 | 0.001 | 0.004 |
| 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".