Ergonomics modelling and evaluation of automobile seat comfort
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it