Statistical and Machine Learning-Based Recognition of Coughing Events Using Triaxial Accelerometer Sensor Data From Multiple Wearable Points
Bibliographic record
Abstract
Recent studies have investigated the use of the accelerometer sensors in smartphones and wearable devices for human activity recognition such as sitting, standing, walking, and laying down with reasonably high accuracy. In this letter, we use a triaxial microelectromechanical accelerometer that is commonly used in smartphones to detect and discern coughing events. Our letter focuses on detecting and differentiating coughing from other human activities such as sitting, standing, and walking using accelerometer's x, y, and z data from various body positions where electronics such as smartphones, watches, headphones, and earphones are commonly worn. Our research compares acceleration measured at five different positions on the body: chest, stomach, shirt-pocket, upper arm, and ear. The measurements are analyzed in the x, y, and z directions using the statistical and machine learning (ML) approaches to study how well coughing activity can be differentiated from acceleration due to other human motions. Analysis of the measured data using both methods show accelerometers mounted on the ear/headphones to be the ideal spot to detect and differentiate coughing with the highest accuracy. ML analysis of accelerometer measurements on ear and chest shows 96 and 93% accuracy, respectively, for cough detection. Measurements show the standard deviation and convolution neural network detection accuracy for cough detection in the ear to be 15 and 3% more sensitive compared to the next best position, which is chest.
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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 | 0.002 |
| 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 | 0.000 | 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 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".