Assessing Driver Comfort and In-Chair Movements in an Automotive Seat with Anti-Fatigue Massage Function
Bibliographic record
Abstract
Prolonged driving is associated with fatigue and reduced comfort, jeopardizing driver safety. This study proposes an innovative Anti-Fatigue Massage Function (AFMF) system integrated into a driver’s seat to improve subjective comfort and decrease compensatory movements during extended driving due to fatigue. In total, 24 participants (12 males, 12 females) completed two 60 min simulated driving sessions—one with the AFMF activated and one with it deactivated. Subjective comfort was measured every 10 min using a 5-point Likert scale, while objective In-Chair Movements (ICMs) were manually recorded by expert researchers from dual-camera recordings. ART ANOVA revealed that the AFMF-equipped seat significantly enhanced comfort ratings and reduced ICM frequencies compared to the deactivated condition. These findings suggest that the AFMF system can enhance driver well-being and mitigate fatigue-related risks during prolonged driving.
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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".