Cough is an early recognizable symptom of COVID-19, which presents a set of challenges for diagnosis because coughs are common symptoms of other medical conditions. The creation of unobtrusive remote monitoring tools for medical professionals that may aid in COVID-19 diagnosis, monitoring and contact tracing could lead to more efficient and accurate treatments, especially in this time of physical distancing.

In this talk, graduate student Madison Cohen-McFarlane will discuss the development of what may be one of the first internationally available-upon-request database of COVID-19 cough events, created by a team of researchers at Carleton University. She will review numerous individual cough events obtained through public media interviews with COVID-19 patients and explain how they can be analyzed through audio-based sensing methods that address the frequency, severity and characteristics of the COVID-19 cough. Their work has also been able to differentiate between different cough types (wet vs. dry), which can help in the diagnosis. She will close by offering insights into how this research can be used for rapid exploration and algorithm development, which can then be applied to more extensive datasets and potentially real time applications.