Low-Cost IoT Based Wearable Respiratory Sensor for Covid-19 Patients

The three main Covid-19 symptoms are shortness of breath, coughing and fever. Currently, most of the patients who tested positive for COVID-19 are self-quarantined at home. Unfortunately, some home quarantine Covid-19 patients are brought in death to hospital. Therefore, e-health remote patient monitoring systems are needed. Although many e-health monitoring systems are proposed by the researcher, not many dedicated systems are suitable for COVID-19 specifically. Mostly do not have a respiratory rate monitoring function. Furthermore, many e-health devices in the market only feature local data storage and do not include Internet of Things (IoT) integration. In this work, we proposed a low-cost IoT based respiratory sensor for home quarantine Covid-19 patients to monitor the respiratory rate. The measured respiratory rate will be transmitted to Google Clould via WiFi connection and the user can read it through their computer or smartphone. Alert message will be generated if the respiratory rate reaches an unsafe threshold. The proposed device was tested with five samples and gave a 100% accuracy on respiratory rate measurement. The proposed prototype cost is much lower than the other respiratory monitoring devices in the market. The proposed device could reduce the mortality of home quarantine Covid-19 patients. © 2022 IJETAE Publication House. All rights reserved.

Internet of things based real-time coronavirus 2019 disease patient health monitoring system

The coronavirus disease (COVID-19) outbreak has led to many infected worldwide and has become a global crisis. COVID-19 manifests in the form of shortness of breath, coughing and fever. More people are getting infected and healthcare systems worldwide are overwhelmed as healthcare workers become exhausted and infected. Thus, remote monitoring for COVID-19 patients is required. An internet of things (IoT) based real-time health monitoring system for COVID-19 patients was proposed. It features monitoring of five physiological parameters, namely electrocardiogram (ECG), heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2) and body temperature. These vitals are processed by the main controller and transmitted to the cloud for storage. Healthcare professionals can read real-time patient vitals on the web-based dashboard which is equipped with an alert service. The proposed system was able to transmit and display all parameters in real-time accurately without any packet loss or transmission errors. The accuracy of body temperature readings, RR, SpO2 and HR, is up to 99.7%, 100%, 97.97% and 98.34%, respectively. Alerts were successfully sent when the parameters reached unsafe levels. With the proposed system, healthcare professionals can remotely monitor COVID-19 patients with greater ease, lessen their exposure to the pathogen, and improve patient monitoring. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Forecasting model of COVID-19 pandemic in Malaysia: An application of time series approach using neural network

COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. One of the measures taken by the Malaysian government, namely the Movement Control Order, has been carried out since March 18, 2020. In order to provide precise information to the government so that it can take the appropriate measures, many researchers have attempted to predict and create the model for these cases to identify the number of cases each day and the peak of this pandemic. Therefore, hospitals and health workers can anticipate a surge in COVID19 patients. In this research, confirmed, recovered, and death cases prediction was performed using the neural network as one of the machine learning methods with high accuracy. The neural network model used is the Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine. The three models calculated the average percentage error (APE) values for 7 days and obtained APE values for most cases less than 10%;only 1 case in the last day of one method had an APE value of approximately 11%. Furthermore, based on the best model, then the forecast is made for the next 7 days. In conclusion, this study identified that the MLP model is the best model for 7-step ahead forecasting for confirmed, recovered, and death cases in Malaysia. However, according to the result of testing data, the ELM performs better than the MLP model. © 2022 by the authors;.

A new hybrid conjugate gradient algorithm for optimization models and its application to regression analysis

The hybrid conjugate gradient (CG) method is among the efficient variants of CG method for solving optimization problems. This is due to their low memory requirements and nice convergence properties. In this paper, we present an efficient hybrid CG method for solving unconstrained optimization models and show that the method satisfies the sufficient descent condition. The global convergence prove of the proposed method would be established under inexact line search. Application of the proposed method to the famous statistical regression model describing the global outbreak of the novel COVID-19 is presented. The study parameterized the model using the weekly increase/decrease of recorded cases from December 30, 2019 to March 30, 2020. Preliminary numerical results on some unconstrained optimization problems show that the proposed method is efficient and promising. Furthermore, the proposed method produced a good regression equation for COVID-19 confirmed cases globally. © 2021 Institute of Advanced Engineering and Science. All rights reserved.

A modified bfgs method via new rational approximation model for solving unconstrained optimization problems and its application

In this paper we present a new BFGS method for solving unconstrained optimization problems, using a modified rational approximation model. The idea is to improve the Barzilai and Borwein approximation (BBA) [27] by incorporating a new parameter. Under certain conditions the global convergent result of the proposed method is established. The numerical results have shown that, the new method is promising and outperforms other classical methods. Besides, the new method was used to solve data from Covid-19 and the performance was compared with Least Square Method (LSM). The outcome has shown that the new method has less relative error and can be used in place of LSM in regression analysis. © 2020, Research Publication. All rights reserved.