Real-Time Patient Location Tracking and Monitoring Geofencing System
Keywords:tracking patients, COVID centers, patient safety, Geofencing, Android app, IMEI number
This study presents a method for tracking patients from COVID centers using geofencing. This article primarily focuses on developing Android applications that can inform people about COVID-19 containment zones and prevent people from entering these zones. Lockdown and quarantine centers have been set up in India and many other countries to reduce the spread. Using a geofencing framework to monitor someone’s Movement during the quarantine period. Geofencing methodologies provide analysis using a layer of geographic boundaries within the original location and Movement. This simulation demonstrates the importance of the distance parameter and gives warnings based on GPS information. This application also notifies when the user enters the security zone and uploads the user’s girlfriend’s IMEI number to an online database. Therefore, this app can be used as a tool to raise public awareness about Maharashtra people’s new need to be vigilant. A geofencing patient tracking system is a location-based service that uses virtual boundaries to track patient movement within a healthcare facility. With this system, geofences are set up around specific areas within a healthcare facility, and when a patient enters or exits one of these areas, their location is automatically recorded by the system. The system can be used to improve patient safety, improve quality of care and prevent unauthorized access to restricted areas.
Lalitha R, Hariharan G, Lokesh N. Tracking the Covid zones through geo-fencing technique. Int J Pervasive Comput Commun. 2020 Jul 10; 16(5): 409–17.
Kedron P, Trgovac AB. Assessing connections and tradeoffs between geospatial data ethics, privacy, and the effectiveness of digital contact tracing technologies. Mapping COVID-19 in Space and Time: Understanding the Spatial and Temporal Dynamics of a Global Pandemic. Springer; 2021; 115–136.
Ahmed N, Michelin RA, Xue W, Ruj S, Malaney R, Kanhere SS, Seneviratne A, Hu W, Janicke H, Jha SK. A survey of COVID-19 contact tracing apps. IEEE Access. 2020 Jul 20; 8: 134577–134601.
Shahroz M, Ahmad F, Younis MS, Ahmad N, Boulos MN, Vinuesa R, Qadir J. COVID-19 digital contact tracing applications and techniques: A review post initial deployments. Transp Eng. 2021 Sep 1; 5: 100072.
Kondylakis H, Katehakis DG, Kouroubali A, Logothetidis F, Triantafyllidis A, Kalamaras I, Votis K, Tzovaras D. COVID-19 mobile apps: a systematic review of the literature. J Medical Internet Res. 2020 Dec 9; 22(12): e23170.
Bode M, Craven M, Leopoldseder M, Rutten P, Wilson M. Contact tracing for COVID-19: New considerations for its practical application. McKinsey & Company; 2020 May 8.
Alqrnawi N, MYDERRİZİ I. COVID-19 Quarantine Monitoring Based on Geofencing Technique. Int J Eng Technol (IJET). 2021 Jul; 7(2): 39–46.
DeCaprio D, Gartner J, Burgess T, Garcia K, Kothari S, Sayed S, McCall CJ. Building a COVID-19 vulnerability index. arXiv preprint arXiv:2003.07347. 2020 Mar 16.
Chaudhuri S, Basu S, Kabi P, Unni VR, Saha A. Modeling the role of respiratory droplets in Covid-19 type pandemics. Phys Fluids. 2020 Jun 1; 32(6): 063309.
Li R, Pei S, Chen B, Song Y, Zhang T, Yang W, Shaman J. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science. 2020 May 1; 368(6490): 489–493.
Singhal A, Singh P, Lall B, Joshi SD. Modeling and prediction of COVID-19 pandemic using Gaussian mixture model. Chaos, Solitons Fract. 2020 Sep 1; 138: 110023.
Moreau VH. Forecast predictions for the COVID-19 pandemic in Brazil by statistical modeling using the Weibull distribution for daily new cases and deaths. Braz J Microbiol. 2020 Sep; 51(3): 1109–1115.