Coughing is a common symptom in many diseases ; however, an objective measurement of cough frequency and severity is lacking in the literature . Although researchers have proposed methods for cough detection, the implementations of the developed algorithms are not publicly available. Furthermore, some of the approaches require patients to wear an accelerometer sensor or other devices, which disturbs the users’ daily activities which makes them impractical in real-world scenarios. In this project, we try to identify the existing baseline of the proposed algorithms to facilitate further research on the cough detection and related topics. Moreover, we investigate design solutions that run on handheld and wearable devices (i.e., smartphone, smartwatch) that perform cough detection with a minimum user's intervention.
In this survey study, we also plan to implement the state-of-the-art of cough detection algorithms   on edge server, smartphone, and smartwatch. Furthermore, we seek to build a docker container of our system along with a fully functional mobile application (perhaps iOS) or Docker image powered by the existing models. Finally, we plan to provide an evaluation of the proposed algorithms to measure their performance using the collected data in our previous study (WearCOPD) .
- Background Reading (October 4)
- CI Environment Setup (October 11)
- Data Annotation and Verification(October 18)
- Model Selection(November 8)
- Final Report (December 13)
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