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An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors

Author

Listed:
  • Hamid Mukhtar

    (Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia)

  • Saeed Rubaiee

    (Department of Industrial and Systems Engineering, College of Engineering, University of Jeddah, Jeddah 21577, Saudi Arabia)

  • Moez Krichen

    (Department of Computer Science, Faculty of Computer Science and Information Technology, Al-Baha University, Al-Baha 65431, Saudi Arabia
    ReDCAD Laboratory, National School of Engineers of Sfax, University of Sfax, Sfax 3038, Tunisia)

  • Roobaea Alroobaea

    (Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia)

Abstract

Experts have predicted that COVID-19 may prevail for many months or even years before it can be completely eliminated. A major problem in its cure is its early screening and detection, which will decide on its treatment. Due to the fast contactless spreading of the virus, its screening is unusually difficult. Moreover, the results of COVID-19 tests may take up to 48 h. That is enough time for the virus to worsen the health of the affected person. The health community needs effective means for identification of the virus in the shortest possible time. In this study, we invent a medical device utilized consisting of composable sensors to monitor remotely and in real-time the health status of those who have symptoms of the coronavirus or those infected with it. The device comprises wearable medical sensors integrated using the Arduino hardware interfacing and a smartphone application. An IoT framework is deployed at the backend through which various devices can communicate in real-time. The medical device is applied to determine the patient’s critical status of the effects of the coronavirus or its symptoms using heartbeat, cough, temperature and Oxygen concentration (SpO 2 ) that are evaluated using our custom algorithm. Until now, it has been found that many coronavirus patients remain asymptomatic, but in case of known symptoms, a person can be quickly identified with our device. It also allows doctors to examine their patients without the need for physical direct contact with them to reduce the possibility of infection. Our solution uses rule-based decision-making based on the physiological data of a person obtained through sensors. These rules allow to classify a person as healthy or having a possibility of infection by the coronavirus. The advantage of using rules for patient’s classification is that the rules can be updated as new findings emerge from time to time. In this article, we explain the details of the sensors, the smartphone application, and the associated IoT framework for real-time, remote screening of COVID-19.

Suggested Citation

  • Hamid Mukhtar & Saeed Rubaiee & Moez Krichen & Roobaea Alroobaea, 2021. "An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors," IJERPH, MDPI, vol. 18(8), pages 1-17, April.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:8:p:4022-:d:534260
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    References listed on IDEAS

    as
    1. Haneen Reda Banjar & Heba Alkhatabi & Nofe Alganmi & Ghaidaa Ibraheem Almouhana, 2020. "Prototype Development of an Expert System of Computerized Clinical Guidelines for COVID-19 Diagnosis and Management in Saudi Arabia," IJERPH, MDPI, vol. 17(21), pages 1-19, November.
    2. Soufiane Belhouideg, 2020. "Impact of 3D printed medical equipment on the management of the Covid19 pandemic," International Journal of Health Planning and Management, Wiley Blackwell, vol. 35(5), pages 1014-1022, September.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Atefeh Hemmati & Amir Masoud Rahmani, 2022. "Internet of Medical Things in the COVID-19 Era: A Systematic Literature Review," Sustainability, MDPI, vol. 14(19), pages 1-29, October.
    2. Mudita Uppal & Deepali Gupta & Sapna Juneja & Adel Sulaiman & Khairan Rajab & Adel Rajab & M. A. Elmagzoub & Asadullah Shaikh, 2022. "Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    3. Andrew D. Madden & Sophie Rutter & Catherine Stones & Wenbo Ai, 2022. "Smart Hand Sanitisers in the Workplace: A Survey of Attitudes towards an Internet of Things Technology," IJERPH, MDPI, vol. 19(15), pages 1-23, August.

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