Cited by 279COVID-19 diagnosis using AINon-Invasive Vital Sign MonitoringSARS-CoV-2 detection and testing

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A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study

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Maghded, Halgurd S., Ghafoor, Kayhan Zrar, Sadiq, Ali Safaa, Curran, Kevin, Rawat, Danda B., Rabie, Khaled·2020·2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)

DOI 10.1109/iri49571.2020.00033

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Abstract

Coronaviruses 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 install 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 computation-rich 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.

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Authors: Maghded, H. S., Ghafoor, K. Z., Sadiq, A. S., Curran, K., Rawat, D. B., & Rabie, K.
Affiliations: Salahaddin University-Erbil (IQ)
University of Wolverhampton (GB)
University of Ulster (GB)
Howard University (US)
Manchester Metropolitan University (GB)
ORCID: Ghafoor, Kayhan Zrar
Sadiq, Ali Safaa
Curran, Kevin
Rawat, Danda B.
Rabie, Khaled
Venue: 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)
Publisher: IEEE
Published: 2020-08
Pages: 180-187
DOI: 10.1109/iri49571.2020.00033
OpenAlex: W3085954833
Cited by: 279
References: 17
Citations by year: 2026: 2 · 2025: 6 · 2024: 12 · 2023: 39 · 2022: 55 · 2021: 114 · 2020: 51
FWCI: 70.67
Type: conference paper
Language: en
Open access: green
hdl.handle.net
Licence: cc-by-nc-nd
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-009 (stm-asf)
doi.org/10.15223/policy-001 (stm-asf)
Locations: IEEE International Conference on Information Reuse and Integration (IRI) (publishedVersion)
Wolverhampton Intellectual Repository and E-Theses (University of Wolverhampton) (acceptedVersion, cc-by-nc-nd)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
Keywords: COVID-19 diagnosis, smartphone sensors, embedded sensors, pneumonia severity prediction, wearable health monitoring, COVID-19 symptoms

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A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study

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