Variability in Body Shape, Superficial Soft Tissue Geometry, and Seatbelt Fit Relative to the Pelvis in Automotive Postures—Methods for Volunteer Data Collection With Open Magnetic Resonance Imaging
Bibliographic record
Abstract
Variability in body shape and soft tissue geometry have the potential to affect the body's interaction with automotive safety systems. In this study, we developed a methodology to capture information on body shape, superficial soft tissue geometry, skeletal geometry, and seatbelt fit relative to the skeleton-in automotive postures-using Open Magnetic Resonance Imaging (MRI). Volunteer posture and belt fit were first measured in a vehicle and then reproduced in a custom MRI-safe seat (with an MR-visible seatbelt) placed in an Open MR scanner. Overlapping scans were performed to create registered three-dimensional reconstructions spanning from the thigh to the clavicles. Data were collected with ten volunteers (5 female, 5 male), each in their self-selected driving posture and in a reclined posture. Examination of the MRIs showed that in the males with substantial anterior abdominal adipose tissue, the abdominal adipose tissue tended to overhang the pelvis, narrowing in the region of the Anterior Superior Iliac Spine (ASIS). For the females, the adipose tissue depth around the lower abdomen and pelvis was more uniform, with a more continuous layer superficial to the ASIS. Across the volunteers, the pelvis rotated rearward by an average of 62% of the change in seatback angle during recline. In some cases, the lap belt drew nearer to the pelvis as the volunteer reclined (as the overhanging folds of adipose tissue stretched). In others, the belt-to-pelvis distance increased as the volunteer reclined. These observations highlight the importance of considering both interdemographic and intrademographic variability when developing tools to assess safety system robustness.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".