Use of Phase Discontinuity Features in Pocket-Worn Accelerometer to detect Coughs
Bibliographic record
Abstract
Cough is considered to be the major symptom of most respiratory problems and due to the recent COVID-19 pandemic cough detection has become a major area of interest. This paper addresses the issue of cough detection using a simple detection methodology using Fast Fourier Transform (FFT) analysis peak detection technique on accelerometer data worn in shirt pocket where smart phones are commonly carried. This methodology measures the peaks on the negative side of the FFT plots indicating phase discontinuity due to coughs or lack of best results are seen on accelerometer signals along directions perpendicular to the chest surface (Z axis) and the vertical height (Y axis) of the test subjects in the different frequency bands 1,2,3 (1-2Hz, 2-4Hz and 4-6Hz). The results indicate that the number peaks in bands 2 and 3 (2-4Hz and 4-6Hz) are significantly higher for detecting cough patterns quite consistently in 2 test subjects of height and weight 1-1.8 meters and 16–64 kilograms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".