Spectral Summation With Machine Learning Analysis of Tri-Axial Acceleration From Multiple Wearable Points on Human Body for Better Cough Detection
Bibliographic record
Abstract
In this letter, coughing is detected using a multiband spectral summation of acceleration signal with machine learning (ML) from various body points where smartphones are commonly worn. A known challenge in this letter is discerning low and mid intensity cough events from noise introduced by walking from the chest, stomach, shirt pocket, upper hand, and ear where smartphones are commonly worn from among seven test subjects of varying heights. Previous studies have shown that coughing during walking can be accurately detected with only 92, 73, 62, and 82% accuracy at the chest, stomach, shirt pocket, and upper hand, respectively, just from raw acceleration signals in the time domain and ML. Newer spectrum analysis show that acceleration measured at these body points along the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> -axis are more spectrally rich during low to medium coughing and walking activity than just walking alone in seven test subjects of varying heights. In this letter, we investigate the use multiband spectral summation features of acceleration measured at these same body points on the torso with ML to improve the accuracy of low/mid intensity cough detection to between 95.2 and 98.2%. At just the chest and upper hand, the spectral sum of acceleration in the 0–5 Hz band shows a 46–142% and 58–136% increase during coughing and walking than just walking alone for different cough intensities and subject heights. This letter is useful in developing future cough-detecting apps on smartphones commonly worn on the torso.
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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 | 0.001 |
| 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".