Health Care Monitoring Application using IoT Devices

Nowadays, health monitoring is crucial, especially monitoring the temperature and heartbeat of the patient in Covid / non-Covid situations. Continued monitoring of the patient is not a possible and tedious job. IoT plays a critical role in Hospitals where patients are in Intensive Care Units (ICU), and patients are treated at home (isolation points). The devices receive data continuously and monitor by the doctors remotely. This paper presents temperature and heartbeat monitoring using Internet Of Things (IoT) devices with an algorithm to capture data from devices and sends it to computer devices at a reasonable cost. Proof Of Concept has been created with the help of an Arduino board, Pulse Sensor, Temperature Sensor, Breadboard, ESP8266 Wi-Fi module, and Liquid Crystal Display (LCD). The IoT devices capture data from different devices (patient health data) in real time. The Health Care Monitoring (HCM) Application builds using microservices architecture, runs on top of the Thingspeak data, and sends notifications to the doctors if there is an emergency. The doctors can act according to rather than monitor continuously. This model eliminates manual intervention for taking the reading from time to time. © 2022 IEEE.

A Development Framework for a Health Monitoring Application in the Power App Platform

Globally, COVID-19 pandemic has influenced and changed norms and common health cultures. Different countries have implemented risk management and dealt with the condition based on the applicability of the international measures and some uniquely to their situations. As technology has become a key tool in daily lives and smart phones and connectivity has become a common necessity for most of the world's population, these can be used to help face the pandemic and the new normal it brings. Using one of the widely used software platforms, the research intends to design a framework for a health monitoring application for private institutions. © 2022 IEEE.

On the Design of Communication-Efficient Federated Learning for Health Monitoring

With the booming deployment of Internet of Things, health monitoring applications have gradually prospered. Within the recent COVID-19 pandemic situation, interest in permanent remote health monitoring solutions has raised, targeting to reduce contact and preserve the limited medical resources. Among the technological methods to realize efficient remote health monitoring, federated learning (FL) has drawn particular attention due to its robustness in preserving data privacy. However, FL can yield to high communication costs, due to frequent transmissions between the FL server and clients. To tackle this problem, we propose in this paper a communication-efficient federated learning (CEFL) framework that involves clients clustering and transfer learning. First, we propose to group clients through the calculation of similarity factors, based on the neural networks characteristics. Then, a representative client in each cluster is selected to be the leader of the cluster. Differently from the conventional FL, our method performs FL training only among the cluster leaders. Subsequently, transfer learning is adopted by the leader to update its cluster members with the trained FL model. Finally, each member fine-tunes the received model with its own data. To further reduce the communication costs, we opt for a partial-layer FL aggregation approach. This method suggests partially updating the neural network model rather than fully. Through experiments, we show that CEFL can save up to to 98.45% in communication costs while conceding less than 3% in accuracy loss, when compared to the conventional FL. Finally, CEFL demonstrates a high accuracy for clients with small or unbalanced datasets. © 2022 IEEE.

IoT and AI-enabled Physical Distance Monitoring Application to Prevent COVID19 Transmission

During COVID19 pandemic, people are encouraged to practice physical distancing at least 1 meter when interacting with other people to prevent the spread of the COVID19. This study aims to develop a system that can monitor the physical distancing and track physical contact in a room using internet of things (IoT) and artificial intelligent technology. The system consists of a small single-board computer (Raspberry Pi), webcam, and web application displaying physical contact information. The system uses YOLO algorithms to detect the human object and euclidean distance formula to determine the distance between human objects. We evaluated the performance of YOLOv3 and YOLOv3-tiny running on Raspberry Pi. The evaluation result shows that YOLOv3 consumes more CPU resources than YOLOv3-tiny but has better accuracy in detecting human objects. YOLOv3-tiny can process images and detect objects faster than YOLOv3. © 2022 IEEE.

Physical Distancing Monitoring Application with Zone-Based Calibration through YOLOv4 Model

Physical Distancing is one of the minimum health protocols where two persons should be at least 1.5 meters apart to lessen the risk of transmission of COVID-19. The study aims to design a real-time monitoring system that detects violations on physical Distancing by applying the You Only Look Once version 4 computer vision model. The program detects the pairwise distance between two persons in a frame and indicates whether they comply with the minimum 1.5 distance between persons. The video frame comprises zone 1 being the farthest from the camera, zone 2, and zone 3 being the nearest from the camera. The program calculates the Euclidean distance between persons and generates a pixel value converted to a metric value by a scale multiplier. The scaling multiplier varies depending on the zone at which the location of the detected person is. The mean absolute error of the distance predicted by the program is at 7.8 centimeters, 5.73 centimeters, and 5.21 centimeters at zones 1, 2, and 3, respectively. The physical distancing detector achieved 95.84% accuracy and 97.08% precision upon evaluating through the confusion matrix. © 2022 IEEE.

RFID Based Identity Authentication System in COVID-19 Era

To control the COVID-19 outbreak, the Malaysia government has to tighten the rules and add on some standard operating procedures (SOP) for all premises. There will be an entrance registration for people that enter any shops, malls, schools, or offices. This entrance registration will take their identities, such as name, contact number, and current temperature. Thus, the government can easily track down and notify the person if the virus transmission occurs. This paper is mainly about improving the daily registration system to monitor the movement of Malaysians during the Covid-19 outbreak. With that needs in mind, a Radio-frequency Identification (RFID) based identity authentication system is developed and presented in this paper. Users do not need to fill in the manual form or scan the Quick Response (QR) code repeatedly, and instead, they are required to just key in the personal data once at the entrance. The RFID tag is applicable to be used as a self-registration at all premises. It can also keep track of the user identity, and the data will be recorded automatically through a monitoring application every time the users enter or leave the premises. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.