Ergonomics modelling and evaluation of automobile seat comfort
Bibliographic record
Abstract
Automobile seats are developed in an iterative manner because subjective feedback, which is usually of questionable quality, drives the design. The time and cost associated with iteration could be justified if the process was guaranteed to produce a comfortable seat. Unfortunately, this is not the case. Current practices are based on the premise that seat system design teams need objective, measurable laboratory standards, which can be linked to subjective perceptions of comfort. Only in this way can predictions be made regarding whether or not a particular design will be viewed by the consumer as comfortable. This type of forecasting ability would effectively improve the efficiency with which automobile seats are designed. In this context, the research reported, developed, and validated a stepwise, multiple linear regression model relating seat interface pressure characteristics, occupant anthropometry, occupant demographics, and perceptions of seat appearance to an overall, subjective comfort index derived from a survey with proven levels of reliability and validity. The model performance statistics were: adjusted r(2)=0.668, standard error of estimate=2.308, F (6, 38)=15.728, p=0.000, and cross-validated r (15)=0.952, p=0.000. From the model, human criteria for seat interface pressure measures were established. These findings could not have been attained without first demonstrating that (1) the data collection protocol for seat interface pressure measurement was repeatable and (2) seat interface pressure measurements can be used to distinguish between seats.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".