Computational Methods Application of Musculoskeletal Analysis for Seating Comfort
Bibliographic record
Abstract
The design of seats in the aerospace industry is more challenging when compared to other industries, such as the automotive industry. The limitations from the different regulations imposed by the aviation regulatory agencies around the world make it difficult to design for comfort with full flexibility, furthermore, weight reduction is a primary design objective that consistently influences design choices in all aspects of aerospace design which tends to add layer of complexity during the design process of aerospace applicable products. The standard seat designing process in the literature includes a combination of computational and experimental methods, such as the use of finite element methods (FEM), pressure mat sensors, or subjective questionnaires. However, the previous methods provide insight into the external forces acting on the human body while also providing the designer of seats with the capability to include multiple computer-simulated design configurations in their design methodology and to compare the results of several design configurations to find the optimal design across different prototypes. The objective of this thesis is to investigate an additional level of insight on comfort levels, by examining internal forces that are occurring in the muscles by using inputs such as external forces and seat postures on a musculoskeletal model. This thesis describes the methodology of simulations, development, and evaluation of comfort evaluation in the neck muscles; Sternocleidomastoid (SCM), and Upper Trapezius as a pilot study of this new approach for comfort evaluation in seat designing processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".