COVID-19 detection using Commercially available off-the-shelf (COTS) components

A group of researchers from Electrical Engineering department recently presented an intuitive and cost-effective method to detect COVID-19. The researchers utilized COTS components to tap into breathing patterns, cough, fever, and workout patterns to gain insights on respiratory tract diseases in general, and COVID-19 in particular. Later, the Machine Learning algorithms can make real-time prognosis that helps in early detection of respiratory tract diseases.

Interested in finding out more?

The research is available at:

O. Ali, M. K. Ishak and M. K. Liaquat Bhatti, “Early covid-19 symptoms identification using hybrid unsupervised machine learning techniques,” Computers, Materials & Continua, vol. 69, no.1, pp. 747–766, 2021.