Intelligent Health Monitoring System for Detection of Symptomatic/Asymptomatic COVID-19 Patient
- 12 July 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Sensors Journal
- Vol. 21 (18), 20504-20511
- https://doi.org/10.1109/jsen.2021.3096425
Abstract
The outbreak of the coronavirus is in its growing stage due to the lack of standard diagnosis for the patients. The situation of any populous area in a geographic location is very critical due to the quick virus spread from an infected individual to the rest. Currently, medical administration is at a crisis point due to the rapidly increasing number of cases and limited medical facilities. Thus, it is time to explore and design an intelligent model to monitor patient health symptoms remotely and predict and detect the abnormality of the patient’s health status in quick succession. Thus, the health status of a coronavirus-affected patient can be identified via a well-adjusted predictive model by analyzing the observed parameters of the health. In the proposed model, an Auto-regressive Integrated Moving Average is incorporated to design a predictive model to find the kth forecast of the observed health symptoms of a patient, and Akaike Information Criteria based selection is introduced to find the current best-fit prediction model. Further, the features are extracted from the forecast over each symptom to find a pattern of each patient, and the patterns are learned by the K-Means algorithm to detect the symptomatic and asymptomatic patient intelligently. To demonstrate the efficiency of the proposed model, we evaluate the model using a synthetic dataset, generated from the health symptoms of 400 patients and compare the performance of the model with the standard methods.This publication has 17 references indexed in Scilit:
- Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020Infectious Disease Modelling, 2020
- Clustering and Classification based real time analysis of health monitoring and risk assessment in Wireless Body Sensor Networksbams, 2019
- A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networksInformation Fusion, 2019
- A survey on big data-driven digital phenotyping of mental healthInformation Fusion, 2019
- Wearable biosensors for healthcare monitoringNature Biotechnology, 2019
- Data Science Algorithms and Techniques for Smart Healthcare Using IoT and Big Data AnalyticsPublished by Springer Science and Business Media LLC ,2019
- Developing residential wireless sensor networks for ECG healthcare monitoringIEEE Transactions on Consumer Electronics, 2017
- Wearable Sensors for Remote Health MonitoringSensors, 2017
- Self-Adaptive Data Collection and Fusion for Health Monitoring Based on Body Sensor NetworksIEEE Transactions on Industrial Informatics, 2016
- Lightweight and Confidential Data Discovery and Dissemination for Wireless Body Area NetworksIEEE Journal of Biomedical and Health Informatics, 2013