[Paper Review] E-Quarantine: A Smart Health System for Monitoring Coronavirus Patients for Remotely Quarantine

Doaa Mohey El-Din, Aboul Ella Hassanein+2 more|arXiv (Cornell University)|May 5, 2020

IoT and Edge/Fog Computing26 references11 citations

TL;DR

E-Quarantine proposes an IoT and AI-powered remote health monitoring system for COVID-19 patients to reduce hospital burden and protect healthcare workers. It continuously collects vital signs—temperature, respiratory rate, pulse, blood pressure—via wearable sensors and enables real-time remote monitoring and clinical decision support, improving patient care while minimizing infection risk.

ABSTRACT

Coronavirus becomes officially a global pandemic due to the speed spreading off in various countries. An increasing number of infected with this disease causes the Inability problem to fully care in hospitals and afflict many doctors and nurses inside the hospitals. This paper proposes a smart health system that monitors the patients holding the Coronavirus remotely. Due to protect the lives of the health services members (like physicians and nurses) from infection. This smart system observes the people with this disease based on putting many sensors to record many features of their patients in every second. These parameters include measuring the patient's temperature, respiratory rate, pulse rate, blood pressure, and time. The proposed system saves lives and improves making decisions in dangerous cases. It proposes using artificial intelligence and Internet-of-things to make remotely quarantine and develop decisions in various situations. It provides monitoring patients remotely and guarantees giving patients medicines and getting complete health care without anyone getting sick with this disease. It targets two people's slides the most serious medical conditions and infection and the lowest serious medical conditions in their houses. Observing in hospitals for the most serious medical cases that cause infection in thousands of healthcare members so there is a big need to uses it. Other less serious patients slide, this system enables physicians to monitor patients and get the healthcare from patient's houses to save places for the critical cases in hospitals.

Motivation & Objective

• To reduce the risk of infection among healthcare providers by enabling remote monitoring of COVID-19 patients.
• To alleviate hospital capacity strain by allowing less severe patients to be monitored at home.
• To support timely clinical decisions through continuous, real-time vital sign tracking using IoT and AI.
• To ensure continuous healthcare delivery without requiring in-person visits, preserving medical resources for critical cases.

Proposed method

• Deploying wearable sensors to continuously collect vital signs—temperature, respiratory rate, pulse rate, blood pressure, and time—every second.
• Transmitting real-time physiological data via IoT to a centralized monitoring platform.
• Applying artificial intelligence algorithms to analyze data streams and detect abnormal health patterns.
• Implementing a dual-tier monitoring approach: critical cases in hospitals and mild-to-moderate cases at home.
• Designing a system architecture that supports remote diagnostics and alerts for healthcare providers.
• Integrating decision-support mechanisms to assist physicians in managing patient conditions without physical contact.

Experimental results

Research questions

• RQ1 How can remote monitoring of COVID-19 patients reduce the risk of infection among healthcare workers?
• RQ2 What IoT and AI-based architecture enables continuous, real-time tracking of vital signs in home-quarantined patients?
• RQ3 How can remote monitoring systems preserve hospital capacity for critical cases during a pandemic?
• RQ4 What role does real-time data analysis play in improving clinical decision-making for non-hospitalized patients?
• RQ5 How can a scalable system ensure reliable healthcare delivery without in-person visits?

Key findings

• The system enables continuous, real-time monitoring of vital signs such as temperature, respiratory rate, pulse rate, and blood pressure every second.
• Remote monitoring reduces the need for in-person hospital visits, thereby minimizing exposure risk for healthcare providers.
• The system supports clinical decision-making by detecting abnormal physiological trends through AI analysis.
• It allows less severe patients to be safely managed at home, reserving hospital beds for critical cases.
• The integration of IoT and AI enhances the scalability and responsiveness of remote patient care during pandemics.
• The proposed framework ensures uninterrupted healthcare delivery and improves patient outcomes through timely interventions.