Innovative IoT-Based Wristlet for Early COVID-19 Detection and Monitoring Among Students

The current global issue of the COVID-19 pandemic has prompted the push and utilization of all available means to halt its spread. COVID-19 is a highly infectious disease, and continuously monitoring early symptoms could help avert catastrophic devastation. This paper proposes an innovative use of the Internet of Things (IoT) enabled system to efficiently and effectively detect early COVID-19 signs at a relatively low cost. This study adopted an experimental approach in designing and constructing a low-cost hardware system using a Wi-Fi enabled microcontroller, a temperature sensor, and a heart rate sensor for students. The proposed system detected and distinguished normal and abnormal temperature, regular and irregular heartbeat and constantly displayed the student's status in a mobile application. Consistent tests proved that the developed IoT-enabled system was reliable, responsive, and cost-effective. The mass production of this device will aid in the early detection of the disease, thereby mitigating the spread among students, particularly in underdeveloped countries. The paper's merit stems from the microcontroller's intelligence programming and the sensor's operation via the mobile application, which enables low-cost early identification of abnormal temperature and heartbeat irregularities. © 2022, Mathematical Modelling of Engineering Problems. All Rights Reserved.

Detection of Coronavirus Disease Using a Novel Machine Learning Approach

In recent times, Coronavirus Disease 2019 (COVID-19) has become a growing concern which has taken the world by surprise. Early detection of this virus can be used to save millions of lives. In this study, a Support Vector Machine (SVM) method is proposed for the identification and classification of C OVID-19 as an early diagnostic method to help clinicians and doctors to accurate distinguish COVID-19 from SARS-CoV-2. A discrete wavelet transform (DWT) algorithm was used to extract features, while SVM was used to classify the extracted features. For the performance evaluation, metrics such as sensitivity (Sens), specificity (Spec), accuracy (Acc), and F-score metrics were used. A detection rate of 98.2% was achieved using the proposed SVM method. Finally, the performance of the SVM method is compared to that of current methods, and it is discovered that the SVM method outperforms them.

Low Cost Sensor Based Hand Washing Solution for COVID-19 Prevention

One of the most effective solutions in the preventive endeavors for infectious diseases such as the COVID-19 pandemic is the application of frequent hand washing. In developing countries, there are several low cost touch-free hand washing solutions involving the use of foot operated mechanisms. However, the use of embedded processors in the design of automatic electronic systems to provide convenience and smarter solutions has in recent times gained unique attention globally. In this paper, we employed an Arduino based microcontroller as processor and ultrasonic based distance sensors to implement a touch-free hand washing mechanism. The microcontroller processes received sensor signals and sends desired command signals to operate two DC motors. A DC pump and servo motor are used to facilitate simple yet effective dispensation of water and soap respectively without any physical contact with the user. The simulation of the developed system was performed with Proteus. The system is also experimentally verified to meet the desired design requirements. © 2021 IEEE.