Cover
Vol. 21 No. 1 (2025)

Published: September 19, 2025

Pages: 221-231

Original Article

Design and Implementation of Monitoring System for Lethal Events of High-Risk COVID-19 Patients

Suhad Qasim Naeem 1 Ammar Ibrahim Majeed 2 Noor Nateq ALfaisaly 3
1Dept. of Information and Communication Engineering, College of Information Engineering, Al-Nahrain University, Baghdad, Iraq
2Dept. of Automation and Artificial Intelligence Engineering, College of Information Engineering, Al-Nahrain University, Baghdad,
3Dept. of Computer Networks Engineering, College of Information Engineering, Al-Nahrain University, Baghdad, Iraq
History
  • Received: June 21, 2024
  • Revised: August 13, 2024
  • Accepted: September 17, 2024
  • Available Online: February 6, 2025
DOI

https://doi.org/10.37917/ijeee.21.1.22

Abstract

The monitoring of COVID-19 patients has been greatly aided by the Internet of Things (IoT). Vital signs, symptoms, and mobility data can be gathered and analyzed by IoT devices, including wearables, sensors, and cameras. This information can be utilized to spot early infection symptoms, monitor the illness’s development, and stop the virus from spreading. It’s critical to take vital signs of hospitalized patients in order to assess their health. Although early warning scores are often calculated three times a day, they might not indicate decompensation symptoms right away. Death rates are higher when deterioration is not properly diagnosed. By employing wearable technology, these ongoing assessments may be able to spot clinical deterioration early and facilitate prompt therapies. This research describes the use of Internet of Things (IoT) to follow fatal events in high-risk COVID-19 patients. These patients’ vital signs, which include blood pressure, heart rate, respiration rate, blood oxygen level, and fever, are taken and fed to a central server on a regular basis so that information may be processed, stored, and published instantly. After processing, the data is utilized to monitor the patients’ condition and send Short Message Service (SMS) alerts when the patients’ vital signs rise above predetermined thresholds. The system’s design, which is based on two ESP32 controllers, sensors for the vital signs listed above, and a gateway, provides real-time reports, high-risk alerts, and patient status information. Clinicians, the patient’s family, or any other authorized person can keep an eye on and follow the patient’s status at any time and from any location. The main contribution in this work is the designed algorithm used in the gateway and the manner in which this gateway collects, analyze, process, and send the patient’s data to the IoT server from one side and the manner in which the gateway deals with the IoT server in the other side. The proposed method leads to reduce the cost and the time the system it takes to get the patient’s status report.

Keywords

References

  1. M. Umair, M. A. Cheema, O. Cheema, H. Li, and H. Lu, “Impact of covid-19 on iot adoption in healthcare, smart homes, smart buildings, smart cities, transportation and industrial iot,” Sensors, vol. 21, no. 11, p. 3838, 2021.
  2. A. H. Talal, E. M. Sofikitou, U. Jaanim¨agi, M. Zeremski, J. N. Tobin, and M. Markatou, “A framework for patient- centered telemedicine: application and lessons learned from vulnerable populations,” Journal of biomedical informatics, vol. 112, p. 103622, 2020.
  3. C. Li, J. Wang, S. Wang, and Y. Zhang, “A review of iot applications in healthcare,” Neurocomputing, p. 127017, 2023.
  4. L. V. Coutts, D. Plans, A. W. Brown, and J. Collomosse, “Deep learning with wearable based heart rate variability 230 | Naeem & Majeed & ALfaisaly for prediction of mental and general health,” Journal of Biomedical Informatics, vol. 112, p. 103610, 2020.
  5. N. Mukati, N. Namdev, R. Dilip, N. Hemalatha, V. Dhi- man, and B. Sahu, “Healthcare assistance to covid-19 pa- tient using internet of things (iot) enabled technologies,” Materials today: proceedings, vol. 80, pp. 3777–3781, 2023.
  6. M. Cascella, M. Rajnik, A. Cuomo, S. C. Dulebohn, and R. Di Napoli, “Features, evaluation and treatment coronavirus (covid-19)[updated 2020 apr 6],” StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing, 2020.
  7. T. H. El Kadi, “Uneven disruption: Covid-19 and the digital divide in the euro-mediterranean region,” IEMed Mediterranian Yearbook, 2020.
  8. L. Bai, D. Yang, X. Wang, L. Tong, X. Zhu, N. Zhong, C. Bai, C. Powell, R. Chen, J. Zhou, et al., “Chinese ex- perts’ consensus on the internet of things-aided diagnosis and treatment of coronavirus disease 2019 (covid-19). clin. ehealth 3, 7–15 (2020),” 2020.
  9. “Memanfaatkan iot dan big data dengan benar bisa putus rantai pandemi covid-19,” 2020.
  10. R. Sharma, A. R. Nair, et al., “Iot-based secure health- care monitoring system,” in 2019 IEEE International Conference on Electrical, Computer and Communica- tion Technologies (ICECCT), pp. 1–6, IEEE, 2019.
  11. W. Jiang, S. Majumder, S. Kumar, S. Subramaniam, X. Li, R. Khedri, T. Mondal, M. Abolghasemian, I. Satia, and M. J. Deen, “A wearable tele-health system towards monitoring covid-19 and chronic diseases,” IEEE Re- views in Biomedical Engineering, vol. 15, pp. 61–84, 2021.
  12. A. Al-Ansi, A. M. Al-Ansi, A. Muthanna, I. A. Elgendy, and A. Koucheryavy, “Survey on intelligence edge com- puting in 6g: Characteristics, challenges, potential use cases, and market drivers,” Future Internet, vol. 13, no. 5, p. 118, 2021.
  13. H. Hijazi, M. Abu Talib, A. Hasasneh, A. Bou Nas- sif, N. Ahmed, and Q. Nasir, “Wearable devices, smart- phones, and interpretable artificial intelligence in com- bating covid-19,” Sensors, vol. 21, no. 24, p. 8424, 2021.
  14. M. D. Santos, C. Roman, M. A. Pimentel, S. Vollam, C. Areia, L. Young, P. Watkinson, and L. Tarassenko, “A real-time wearable system for monitoring vital signs of covid-19 patients in a hospital setting,” Frontiers in Digital Health, vol. 3, p. 630273, 2021.
  15. J. H. Kim, W. S. Choi, J. Y. Song, Y. K. Yoon, M. J. Kim, and J. W. Sohn, “The role of smart monitoring dig- ital health care system based on smartphone application and personal health record platform for patients diag- nosed with coronavirus disease 2019,” BMC infectious diseases, vol. 21, pp. 1–8, 2021.
  16. V. Suraj, C. Del Vecchio Fitz, L. B. Kleiman, S. K. Bhavnani, C. Jani, S. Shah, R. R. McKay, J. Warner, and G. Alterovitz, “Smart covid navigator, a clinical decision support tool for covid-19 treatment: Design and development study,” Journal of Medical Internet Research, vol. 24, no. 2, p. e29279, 2022.
  17. S. Ashique, N. Mishra, S. Mohanto, A. Garg, F. Taghizadeh-Hesary, B. J. Gowda, and D. K. Chellap- pan, “Application of artificial intelligence (ai) to control covid-19 pandemic: Current status and future prospects,” Heliyon, 2024.
  18. M. Aal-Nouman, H. Takruri-Rizk, and M. Hope, “Trans- mission of medical messages of patient using control signal of cellular network,” Telematics and Informatics, vol. 35, no. 1, pp. 267–281, 2018.
  19. B. Myroniv, C.-W. Wu, Y. Ren, A. Christian, E. Bajo, and Y.-C. Tseng, “Analyzing user emotions via physi- ology signals,” Data Science and Pattern Recognition, vol. 1, no. 2, pp. 11–25, 2017.
  20. J. J. Duair, A. I. Majeed, and G. M. Ali, “Design and implementation of iot-based scada for a multi microgrid system,” ECS Transactions, vol. 107, no. 1, p. 17345, 2022.
  21. A. Shaout and B. Crispin, “Using the mqtt protocol in real time for synchronizing iot device state.,” Int. Arab J. Inf. Technol., vol. 15, no. 3A, pp. 515–521, 2018.
  22. Z. Zhang, L. Wang, R. Liu, and J. Fan, “Development of cloud computing platform based on neural network,” Mathematical Problems in Engineering, vol. 2022, no. 1, p. 1513081, 2022.
  23. L. S. Wilson and A. J. Maeder, “Recent directions in telemedicine: review of trends in research and practice,” Healthcare informatics research, vol. 21, no. 4, pp. 213– 222, 2015.
  24. X. Zeng, “The impacts of electronic health record imple- mentation on the health care workforce,” North Carolina medical journal, vol. 77, no. 2, pp. 112–114, 2016.
  25. A. Y. Mahdi and S. S. Yuhaniz, “Automatic extraction of knowledge for diagnosing covid-19 disease based on text 231 | Naeem & Majeed & ALfaisaly mining techniques: A systematic review,” Periodicals of Engineering and Natural Sciences, vol. 9, no. 2, pp. 918– 929, 2021.
  26. A. E. Ezugwu, I. A. T. Hashem, O. N. Oyelade, M. Al- mutari, M. A. Al-Garadi, I. N. Abdullahi, O. Otegbeye, A. K. Shukla, and H. Chiroma, “A novel smart city-based framework on perspectives for application of machine learning in combating covid-19,” BioMed Research In- ternational, vol. 2021, 2021.
  27. M. Jabraeil Jamali, B. Bahrami, A. Heidari, P. Al- lahverdizadeh, and F. Norouzi, “Towards the internet of things: Architectures, security, and applications,” 2019.