Analysis of Frequency Spectral Features of Coughing Activity from Tri-Axial Accelerometer sensor at Multiple Body Points
Bibliographic record
Abstract
Human activity recognition using sensors has wider applications such as daily activity and health monitoring, robotics, security purpose, monitoring human beings in the workplace, and others. Activities such as sitting, standing, walking, walking upstairs, and walking downstairs are commonly classified. Cough event detection and counting have always been the most important research topic in the medical field. We aim to study the non-cough and cough activity in human beings at five body positions of varying heights subjects. Previous studies have shown that cough during walking can be accurately detected with 92, 73, 62, and 82% accuracy at the chest, stomach, shirt pocket, and upper hand respectively from raw acceleration signals in the time domain. We analyzed the frequency domain characteristics, the Spectral Maximum (SM), and Spectral Summation (SS) at four frequency bands in the 0–20 Hz range for the accelerometer axis: x, y, and z. Our study reveals a 24 – 142 % increase along the Y-axis and a 14 – 146 % increase along the Z-axis in SS of cough signal compared to a non-cough signal at the five-position considered in our study. Evaluation of the 3D plot of spectral features shows the clear difference of a cough signal from a non-cough.
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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) | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".