ABSTRACT
The COVID-19 pandemic coincided with the growth and ripeness of several digital methods, such as Artificial Intelligence (AI) (including Machine Learning (ML) and Deep Learning (DL)), internet of things (IoT), big-data analytics, Software Defined Network (SDN), robotic technology, and blockchain, etc. resulting in an experience chance for telemedicine advancement. In several nations, a telemedicine platform based on digital technology has been built and integrated into the clinical workflow in a variety of modes, including many-To-one, one-To-many, consultation mode, and practical-operation modes. These platforms are practical, efficient, and successful for exchanging epidemiological data, facilitating face-To-face interactions between patients or healthcare professionals over long distances, lowering the risk of disease transmission, and enhancing patient outcomes. This article provides a Systematic Literature Review (SLR) to call attention to the most recent advancements in evaluating COVID-19 data utilizing various methodologies such as ML, DL, SDN, and IoT. The number of studies on ML and DL provided and reviewed in this article has proven a considerable effect on the prediction and spreading of COVID-19. The main goal of this study is to show how ML, DL, IoT, and SDN may be used by researchers to provide significant solutions for authorities and healthcare statements to lessen the influence of pestilence. This report also includes many novel strategies for raising the prevalent telemedicine use. © 2022 IEEE.
ABSTRACT
In these recent years, the world has witnessed a kind of social exclusion and the inability to communicate directly due to the Corona Virus Covid 19 (COVID-19) pandemic, and the consequent difficulty of communicating with patients with hospitals led to the need to use modern technology to solve and facilitate the problem of people communicating with each other. healthcare has made many remarkable developments through the Internet of things (IoT) and cloud computing to monitor real-time patients' data, which has enabled many patients' lives to be saved. This paper presents the design and implementation of a Private Backend Server Software based on an IoT health monitoring system concerned with emergency medical services utilizing biosensors to detect multi-vital signs of an individual with an ESP32 microcontroller board and IoT cloud. The device displays the vital data, which is then uploaded to a cloud server for storage and analysis over an IoT network. Vital data is received from the cloud server and shown on the IoT medical client dashboard for remote monitoring. The proposed system allows users to ameliorate healthcare jeopardy and minify its costs by re-cording, gathering, sharing, and analyzing vast biodata streams such as Intensive Care Units (ICU) (i.e., temperature, heartbeat rate (HR), Oxygen level (SPO2), etc.), efficiently in real-time. In this proposal, the data is sent from sensors fixed in the patient body to the Web and Mobile App continually in real time for collection and analysis. The system showed impressive performance with an average disparity of less than 1%. body temperature, SPO2, and HR readings were remarkably accurate compared to the CE approval patient monitoring system. In Addition, The system was highly dependable with a success rate for IoT data broadcasts. © 2023,International journal of online and biomedical engineering. All Rights Reserved.