| Paper ID | SS-10.2 |
| Paper Title |
COUGHWATCH: REAL-WORLD COUGH DETECTION USING SMARTWATCHES |
| Authors |
Daniyal Liaqat, Salaar Liaqat, Jun Lin Chen, Tina Sedaghat, Moshe Gabel, Frank Rudzicz, Eyal de Lara, University of Toronto, Canada |
| Session | SS-10: Computer Audition for Healthcare (CA4H) |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Special Sessions: Computer Audition for Healthcare (CA4H) |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Continuous monitoring of cough may provide insights into the health of individuals as well as the effectiveness of treatments. Smartwatches, in particular, are highly promising for such monitoring: they are inexpensive, unobtrusive, programmable, and have a variety of sensors. However, current mobile cough detection systems are not designed for smartwatches, and perform poorly when applied to real-world smartwatch data since they are often evaluated on data collected in the lab. In this work we propose CoughWatch, a lightweight cough detector for smartwatches that uses audio and movement data for in-the-wild cough detection. On our in-the-wild data, CoughWatch achieves a precision of 82% and recall of 55%, compared to 6% precision and 19% recall achieved by the current state-of-the-art approach. Furthermore, by incorporating gyroscope and accelerometer data, CoughWatch improves precision by up to 15.5 percentage points compared to an audio-only model |
