The Effect of Anthropometric Variations on Acoustical Flow Estimation: Proposing a Novel Approach for Flow Estimation Without the Need for Individual Calibration
Bibliographic record
Abstract
Tracheal sound average power is directly related to the breathing flow rate and recently it has attracted considerable attention for acoustical flow estimation. However, the flow-sound relationship is highly variable among people and it also changes for the same person at different flow rates. Hence, a robust model capable of estimating flow from tracheal sounds at different flow rates in a large group of individuals does not exist. In this paper, a model is proposed to estimate respiratory flow from tracheal sounds. The proposed model eliminates the dependence of the previous methods on calibrating the model for every individual and at different flow rates. To validate the model, it was applied to the respiratory sound and flow data of 93 healthy individuals. We investigated the statistical correlation between the model parameters and anthropometric features of the subjects. The results have shown that gender, height, and smoking are the most significant factors that affect the model parameters. Hence, we grouped nonsmoker subjects into four groups based on their gender and height. The average of model parameters in each group was defined as the group-calibrated model parameters. These models were applied to estimate flow from data of subjects within the same group and in the other groups. The results show that flow estimation error based on the group-calibrated model is less than 10%. The low estimation errors confirm the possibility of defining a general flow estimation model for subjects with similar anthropometric features with no need for calibrating the model parameters for every individual. This technique simplifies the acoustical flow estimation in general applications including sleep studies and patients' screening in health care facilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".