A novel AI-enabled framework to diagnose Coronavirus COVID-19 using smartphone embedded sensors: design study
AuthorsMaghded, Halgurd S
Ghafoor, Kayhan Zrar
Rawat, Danda B
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AbstractCoronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Further, nowadays, all the world countries are striving to control the COVID-19. There are many mechanisms to detect coronavirus including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost, taking time to instal them and use. Therefore, in this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Today’s smartphones are powerful with existing computationrich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors’ signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease.
CitationMaghded, H.S., Ghafoor, K.Z., Sadiq, A.S., Curran, K., Rawat, D.B and Rabie, K. (2020) A novel AI-enabled framework to diagnose Coronavirus COVID-19 using smartphone embedded sensors: design study. 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI). 11th-13th August, 2020, Las Vegas, NV, USA, pp. 180-187. DOI: 10.1109/IRI49571.2020.00033
DescriptionThis is an accepted manuscript of an article published by IEEE (in press). The accepted version of the publication may differ from the final published version.
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