Human Respiratory Sound Classification for Remote Health Monitoring Applications
Bibliographic record
Abstract
Sensor integration as a means of remote health monitoring is a growing area of research, especially among the older adult population as a means of supporting living independently.Chronic respiratory conditions require careful monitoring and evaluation in order to ensure health.In this work, the application of audio-based methods to monitor respiratory sounds (e.g., cough) is presented as a means to identify abrupt changes in health status.Three main classification tasks (C wet cough vs. dry cough vs. whooping cough vs.restricted breathing, C wet cough vs. dry cough, and C cough vs. restricted breathing) are evaluated using three main approaches: classical machine learning, transfer learning based on standard image classifiers, and audio classifiers.For the image-based transfer learning approach, several audio visualization methods were considered including an aggregateimage method that combined the top three performing visualization methods.Overall, the aggregate-based image classifier had the best performance for C , C , and C with weighted F -scores of ., ., and .respectively.In light of the COVID-pandemic, a novel COVID-spontaneous (reflex) cough database (NoCoCoDa) was also collected from public media interviews.Finally, performance factors associated with external sources that may affect sound recordings are also investigated.The respiratory monitoring methods described in this thesis are designed to be expanded on in future work eventually leading to a respiratory sound measurement system imbedded in a smart home environment aimed to support older adults age independently.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".