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COVID-19

Revolutionizing Healthcare: IoT-Driven Approach to Remote Patient Monitoring and Management

Strategic Insights

— 2 min read

A team of researchers from King Khalid University, Princess Nourah bint Abdulrahman University, Northern Border University, and other institutions have developed a Smart Health Monitoring System (SHMS) that utilizes Internet of Things (IoT) and machine learning (ML) to track patients' vital signs and environmental conditions in real-time. The system's primary goal is to provide continuous and remote monitoring, making it suitable for real-world healthcare deployment during global health emergencies. The researchers claim that their solution offers an effective and accessible tool for early detection, timely intervention, and reduced burden on healthcare infrastructure.

Key Takeaways:

  • The SHMS uses IoT and ML to track patients' vital signs, including Body Temperature (BT), Heart Rate (HR), Environmental Temperature (ET), and Environmental Humidity (EH), with a focus on COVID-19 and influenza outbreaks.
  • Sensor-based data acquisition is managed through microcontroller hardware, with data transmitted via Wi-Fi to cloud-based platforms accessible through smartphones, laptops, and other internet-enabled devices.
  • The system processes data using machine learning models-Logistic Regression and Decision Tree-to predict the patient's health status with high accuracy, with the Logistic Regression model outperforming the Decision Tree model at an accuracy rate of 88.89%.
  • The SHMS architecture is scalable, supporting multi-user environments and large numbers of connected devices, making it suitable for real-world healthcare deployment during global health emergencies.
  • The system can be extended to measure additional parameters such as Respiratory Rate (RR) and Oxygen Saturation (SpO2) to enhance pandemic responsiveness.
  • The researchers conclude that this solution offers an effective and accessible tool for early detection, timely intervention, and reduced burden on healthcare infrastructure, especially during health crises.
  • The study involved a collaboration between researchers from King Khalid University, Princess Nourah bint Abdulrahman University, Northern Border University, and other institutions.

Statistics:

  • The SHMS has an accuracy rate of 88.89% in predicting patient health status using the Logistic Regression model.
  • The system can support multi-user environments and large numbers of connected devices.
  • The SHMS can be extended to measure additional parameters such as Respiratory Rate (RR) and Oxygen Saturation (SpO2) to enhance pandemic responsiveness.

Sources:

  • Alexandria Engineering Journal (Elsevier) - "Revolutionizing Healthcare: an IoT-driven Approach to Remote Patient Health Monitoring and Management During the Pandemic and Beyond"
  • TB & Outbreaks Week (NewsRx LLC) - "Reports from Northern Border University Provide New Insights into COVID-19 (Revolutionizing Healthcare: an IoT-driven Approach To Remote Patient Health Monitoring and Management During the Pandemic and Beyond)"
  • NewsRx LLC - Copyright 2025, NewsRx LLC
  • Elsevier - www.elsevier.com
  • Alexandria Engineering Journal - www.journals.elsevier.com/alexandria-engineering-journal/

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