A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study
Open accessAbstract
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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An Analysis of Body Language of Patients Using Artificial Intelligence
Abdulghafor, R. A. A., Abdelmohsen, A., Turaev, S., Ali, M. A. H., & Wani, S. (2022) · Healthcare · doi:10.3390/healthcare10122504 · Cited by 28 · see citing works
Deep learning model for detection of COVID-19 utilizing the chest X-ray images
Gafoor, S. A., Sampathila, N., Madhushankara, M., & Swathi, K. S. (2022) · Cogent Engineering · doi:10.1080/23311916.2022.2079221 · Cited by 27 · see citing works
Bankuoru Egala, S., & Liang, D. (2024) · European Journal of Information Systems · doi:10.1080/0960085x.2023.2251927 · Cited by 30 · see citing works
The Power of Augmented Reality and Artificial Intelligence During the Covid-19 Outbreak
Kerdvibulvech, C., & Chen, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60117-1_34 · Cited by 26 · see citing works
Ertaş, Y. N., Mahmoodi, M., Shahabipour, F., Jahed, V., Di̇ltemi̇z, S. E., Tutar, R., & Ashammakhi, N. A. (2021) · Emergent Materials · doi:10.1007/s42247-021-00165-x · Cited by 26 · see citing works
Hybrid PSO–SVM algorithm for Covid-19 screening and quantification
Sheela, M. A. A., & Arun, C. A. (2022) · International Journal of Information Technology · doi:10.1007/s41870-021-00856-y · Cited by 26 · see citing works
Role of Artificial Intelligence in fighting against COVID -19
Nirmala, A. P., & More, S. (2020) · IEEE International Conference on Advances and Developments in Electrical and Electronics Engineering (ICADEE) · doi:10.1109/icadee51157.2020.9368956 · Cited by 25 · see citing works
Secure Monitoring System for IoT Healthcare Data in the Cloud
Stergiou, C. L., Plageras, A. P., Memos, V. A., Koidou, M. P., & Psannis, K. E. (2023) · Applied Sciences · doi:10.3390/app14010120 · Cited by 24 · see citing works
Covid-19 Prevention Kit Based on an Infrared Touchless Thermometer and Distance Detector
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RetractedANFIS for prediction of epidemic peak and infected cases for COVID-19 in India
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COVID-19, Sensors, and Internet of Medical Things (IoMT)
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Data Mining on Smartphones: An Introduction and Survey
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Intelligent Decision Support System for COVID-19 Empowered with Deep Learning
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Swarm Intelligence and Evolutionary Machine Learning Algorithms for COVID-19
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Prediction of global spread of COVID-19 pandemic: a review and research challenges
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Self-citation
- 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)
- Venue
- 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)
- Publisher
- IEEE
- Published
- 2020-08
- Pages
- 180-187
- 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
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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