Biomechanics of Volunteers Subject to Loading by a Motorized Shoulder Belt Tensioner
Bibliographic record
Abstract
STUDY DESIGN: A biomechanical study using human volunteers. OBJECTIVE: Motorized shoulder belt tensioning is a new seatbelt technology that is likely to be incorporated into future vehicles. The objective of this study was to characterize the upper torso biomechanics of 3 sizes of adult volunteers (5th percentile female, 50th percentile male, and 95th percentile male) subjected to motorized shoulder belt tensioning in the static environment. SUMMARY OF BACKGROUND DATA: There is a lack of volunteer data concerning the biomechanics of occupants subject to motorized precrash shoulder belt tensioning. Studies of torso repositioning by the air force for ejection seats are much too aggressive to be relevant to motorized systems. Low-level motorized shoulder belt tensioning is well tolerated by vehicle occupants but optimized performance by occupant size is unknown. METHODS: Nineteen male and 6 female subjects were instrumented in a fixture designed to support the occupant leaning forward and apply seatbelt tension. The subjects were 5th percentile females, 50th percentile males, and 95th percentile males. Reflective markers were placed on the subjects to monitor torso kinematics during tensioning. RESULTS: Changes in spinal curvature were small during shoulder belt tensioning and the angular motion of the torso originated within 4.2 cm of the pelvis-femur junction or H-point. Torso repositioning and retraction timing was found to be: 54.3 degrees in 0.78 seconds for the 5th percentile female, 57.6 degrees in 0.95 seconds for the 50th percentile male, and 42.2 degrees in 0.92 seconds for the 95th percentile male. CONCLUSION: Occupant size has a significant effect on retraction time to reposition the torso during shoulder belt tensioning. Larger vehicle occupants require more time because of a slower retraction velocity. The results are sufficiently simple that a lumped-mass model can predict tensioning kinetics.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".