University of Cambridge researchers develop machine learning app to collect the sounds of Covid-19
Researchers at the University of Cambridge have developed an app that will collect the sounds of Covid-19.
The Covid-19 Sounds App will be used to gain data to develop machine learning algorithms that could automatically detect whether a person is suffering from the disease.
It would be based on the sound of their voice, their breathing and coughing.
“There’s still so much we don’t know about this virus and the illness it causes, and in a pandemic situation like the one we’re currently in, the more reliable information you can get, the better,” said Professor Cecilia Mascolo from Cambridge’s department of computer science and technology, who led the development of the app.
Being a respiratory condition, the sounds made by people with the condition, including voice, breathing and cough sounds, are very specific.
A large, crowdsourced data set will be useful in developing machine learning algorithms that could be used for automatic detection of the condition.
The app collects basic demographic and medical information from users, as well as spoken voice samples, breathing and coaching samples through the phone’s microphone.
It will ask users if they have tested positive for the coronavirus, and collect one coarse grain location sample.
But it will not track users and will only collect location data once when users are actively using it.
Data will be stored on university servers and used solely for research purposes.
Once the initial analysis of the collected data has been completed, it will be released to other researchers and could help shed light on disease progression or the further relationship of the respiratory complication with medical history, for example.
“Having spoken to doctors, one of the most common things they have noticed about patients with the virus is the way they catch their breath when they’re speaking, as well as a dry cough, and the intervals of their breathing patterns,” said Prof Mascolo.
“There are very few large datasets of respiratory sounds, so to make better algorithms that could be used for early detection, we need as many samples from as many participants as we can get.
“Even if we don’t get many positive cases of coronavirus, we could find links with other health conditions.”
The study has been approved by the ethics committee of the department of computer science and technology, and is partly funded by the European Research Council through Project EAR.
Professor Pietro Cicuta, from from Cambridge’s Cavendish Laboratory and a member of the team behind the app’s development, said: “I am amazed at the speed that we managed to connect across the University to conceive this project, and how Cecilia's team of developers came together to respond to the urgency of the situation.”
The app is available as a web app, and versions for Android and iOS will be available soon.