  Cough is a major symptom of respiratory-related diseases. There exists a
tremendous amount of work in detecting coughs from audio but there has been no
effort to identify coughs from solely inertial measurement unit (IMU). Coughing
causes motion across the whole body and especially on the neck and head.
Therefore, head motion data during coughing captured by a head-worn IMU sensor
could be leveraged to detect coughs using a template matching algorithm. In
time series template matching problems, K-Nearest Neighbors (KNN) combined with
elastic distance measurement (esp. Dynamic Time Warping (DTW)) achieves
outstanding performance. However, it is often regarded as prohibitively
time-consuming. Nearest Centroid Classifier is thereafter proposed. But the
accuracy is comprised of only one centroid obtained for each class.
Centroid-based Classifier performs clustering and averaging for each cluster,
but requires manually setting the number of clusters. We propose a novel
self-tuning multi-centroid template-matching algorithm, which can automatically
adjust the number of clusters to balance accuracy and inference time. Through
experiments conducted on synthetic datasets and a real-world earbud-based cough
dataset, we demonstrate the superiority of our proposed algorithm and present
the result of cough detection with a single accelerometer sensor on the earbuds
platform.
