Since the Novel Coronavirus hit the world back in January of this year, we have seen many companies and organisations create ways to track COVID-19 — the disease caused by Coronavirus. Now, there is a flexible band-aid-like device that can monitor for COV-19 symptoms by analysing coughs and breathing of a person.

The device, built by the Northweatern University of Chicago in collaboration with Shirley Ryan AbilityLab, sits at the base of a person’s throat and monitors his/her coughing and breathing to predict if that person is infected.

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Image: Northewestern University

It was previously a device that was used to monitor speech and swallowing in stroke-hit patients in recovering stages. However, the team tweaked the device to track the cough and breathing troubles (primary for COV-19 patients) of a person.

Now, the device does not use a microphone as it is too intrusive for a person’s privacy. Instead, it uses a “high-bandwidth, tri-axis accelerometer to measure the movement of the surface of the skin”, according to the Director of Northwestern University’s Center on Bio-Integrated Electronics, John Rogers.

As it is a small flexible device integrated with a sensor, it does not have any ports to enable a wired connection. So, users can place the device on a wireless charger once a day to charge it up. While the device charges up, it also uploads all the data collected to a nearby iPad, which then uploads it to an HIPAA-approved cloud. Once the upload is completed, a proprietary AI analyses the data for any irregularities that are correlated with COVID-19.

The team is delivering the device to various healthcare institutions bundled with an iPad and the wireless charger in the box.

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Image: Northewestern University

Now, as the device is still in its early stages, the already AI-analysed data are double verified by human operators before they are sent to medical facilities. As of now, twenty-five people have been wearing the device for two weeks and as a result, around 1 TB of data has been uploaded to the cloud sized which is consist data of about 1500 hours.

VIACNET

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