Pressure prediction for the personalized and automatic fitting of respiratory masks
Bibliographic record
Abstract
Respiratory masks are important protection equipment for healthcare workers. User's discomfort due to a poor fit of respiratory masks over a very long period is a serious concern. The objective of this study is to predict the user's fit through prediction of mask's facial pressure on a mobile device. Personalized 3D printed masks were designed based on 60 face geometries. 3D scans and finite element analysis (FEA) results were used to develop machine learning (ML) algorithms for predicting pressures at the face-mask interface. Random Forest Regressor, Decision Tree Regressor, and Elastic Net were tested after standardizing input data. Predictions were made for 15 levels of mask tightening using linear force. Error indicators as Mean Absolute Percentage Error (MAPE), Median Absolute Error (MAE) and Root Mean Square Error (RMSE) were assessed, and the predicted mesh was calibrated against the FEA model using the Iterative Closest Point (ICP) algorithm. The models demonstrated their feasibility in reproducing the FEA results, with Random Forest Regressor providing the best pressure prediction (1.875 RMSE, 0.169 MAPE) and convincing 3D mesh results, respecting a 5% tolerance threshold. ML models were shown as feasible surrogates to FEA for eventual use on a mobile device.
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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 | 0.000 |
| 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 | 0.001 | 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".