  Cough monitoring can enable new individual pulmonary health applications.
Subject cough event detection is the foundation for continuous cough
monitoring. Recently, the rapid growth in smart hearables has opened new
opportunities for such needs. This paper proposes EarCough, which enables
continuous subject cough event detection on edge computing hearables by
leveraging the always-on active noise cancellation (ANC) microphones.
Specifically, we proposed a lightweight end-to-end neural network model --
EarCoughNet. To evaluate the effectiveness of our method, we constructed a
synchronous motion and audio dataset through a user study. Results show that
EarCough achieved an accuracy of 95.4% and an F1-score of 92.9% with a space
requirement of only 385 kB. We envision EarCough as a low-cost add-on for
future hearables to enable continuous subject cough event detection.
