Personalized Fitting of Respiratory Mask Using 3D Numerical Simulation and Finite Element Analysis
Bibliographic record
Abstract
Respiratory masks, such as N95, are widely used in clinical and industrial environments because of their high filtration capacity.However, prolonged wear could provide discomfort due to poor fitting to each individual's face's exact morphology and excessive tightening.This study aims to personalize the design of respiratory masks and simulate the fitting using finite element analysis.A cohort of 7 participants was recruited to evaluate the fit of a virtual 3D mask.A scan of the face was performed on an iPhone by an app using ARKit to acquire a geometric model for simulation.The mask pressure and seal were calculated digitally using Ansys Mechanical after importing the 3D geometries of the mask and the face.An algorithm allows to place the mask in front of the face without inter-penetration.Facial soft tissues were accounted as a homogeneous hyperelastic material model.The silicone was modeled using hyperelastic material properties and the mask was considered as rigid.A pressure map illustrates the pattern that the mask will produce on a given user's face, in order to assert the desired comfort criteria.A map of the gap between the mask and the face shows the sealing capability of the mask.The pressure points of the silicone on the face were simulated after tightening the mask.The pressure pattern must be uniform and without pressure peaks to ensure user comfort.To ensure the consistency of the numerical results, experimental pressure measurements were also performed on the participants and their dedicated masks.Facial pressure calculation and measurement tests were performed under 3 levels of tightening (low = 5N, medium = 13N and high = 20N).The outcome of this study could provide major insights in the design of respiratory masks through face scanning technologies and numerical simulation.Moreover, it could contribute to fully customize the respiratory mask to the user's face, for enhanced comfort and proper sealing.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".