Analysis and modeling of human seat interaction with a focus on the upper body and backrest using biomechanics and contact mechanics
Bibliographic record
Abstract
BACKGROUND: This paper outlines a method to study the interaction between the human body and the aircraft seat concerning the seat comfort. METHOD: Firstly, the human body is modeled based on biomechanics and divided into a number of body segments connected by joints according to human anatomy. The angles between the body segments are obtained by curve fitting of the existing biomechanical research data. The contact forces between the human body and the seat are modeled using pairs of bi-lateral point forces. These forces are calculated and located through the analysis of the center of gravity of each body segment and average muscular structure of the human body. The geometry of the human and the seat is obtained from a 3D scan model or a CAD model. Secondly, the pressure distribution between the human body and the seat is modeled and calculated using the contact stress theory. The results of the two parts are combined to analyze the comfortability in relation to different postures, backrest recline angles and changing in shape and material. RESULTS: Simulations were performed and they are compared with experimental measurement and various FEM studies for validation. It is found that accuracy of this method is comparable with most FEM calculation. CONCLUSION: This method provides a new direction in cushion conform research. It is faster and convenient to use comparing to the FEM, and the result is reliable.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".