Smart healthcare support for remote patient monitoring during covid-19 quarantine

Taiwo, Olutosin; Ezugwu, Absalom E.

doi:10.1016/j.imu.2020.100428

Cited by 159 publications

(96 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this epidemic control measure, it is determined that whether people are properly practicing self-quarantine or not. The reason behind monitoring self-quarantine is to preserve violations or non-compliance, which may spread the virus to the community [27,28]. During this pandemic times, the quarantine/social-distancing monitoring remains the main focus of the health authorities.…”

Section: Quarantine/social-distancing Monitoringmentioning

confidence: 99%

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Majeed

Lee

2021

Inventions

View full text Add to dashboard Cite

With the advent of the pandemic (e.g., novel corona virus disease 2019 (COVID-19)), a tremendous amount of data about individuals are collected by the health authorities on daily basis for curbing the disease’s spread. The individuals’ data collection/processing at a massive scale for community well-being with the help of digital solutions (e.g., mobile apps for mobility and proximity analysis, contact tracing through credit card usage history, facial recognition through cameras, and crowd analysis using cellular networks data etc.) raise several privacy concerns. Furthermore, the privacy concerns that are arising mainly due to the fine-grained data collection has hindered the response to tackle this pandemic in many countries. Hence, acquiring/handling individuals data with privacy protection has become a vibrant area of research in these pandemic times. This paper explains the shift in privacy paradigm due to the pandemic (e.g., COVID-19) which involves more and detailed data collection about individuals including locations and demographics. We explain technical factors due to which the people’s privacy is at higher risk in the COVID-19 time. In addition, we discuss privacy concerns in different epidemic control measures (ECMs) (e.g., contact tracing, quarantine monitoring, and symptoms reporting etc.) employed by the health authorities to tackle this disease. Further, we provide an insight on the data management in the ECMs with privacy protection. Finally, the future prospects of the research in this area tacking into account the emerging technologies are discussed. Through this brief article, we aim to provide insights about the vulnerability to user’s privacy in pandemic times, likely privacy issues in different ECMs adopted by most countries around the world, how to preserve user’s privacy effectively in all phases of the ECMs considering relevant data in loop, and conceptual foundations of ECMs to fight with future pandemics in a privacy preserving manner.

show abstract

Section: Quarantine/social-distancing Monitoringmentioning

confidence: 99%

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Majeed

Lee

2021

Inventions

View full text Add to dashboard Cite

show abstract

“…WBSN plays a critical role in monitoring vital signs of the patients in a regular time interval to improve the COVID-19 disease prediction [7] , [8] . Also, the data fusion techniques are highly used to integrate the raw sensor data from several monitoring devices and produce data in a more consistent, accurate, and useful form [9] , [10] .…”

Section: Related Workmentioning

confidence: 99%

iCovidCare: Intelligent health monitoring framework for COVID-19 using ensemble random forest in edge networks

Adhikari

Munusamy

2021

Internet of Things

View full text Add to dashboard Cite

“…The allocation of limited resources in a rapidly expanding pandemic entails a difficult decision for the distribution of scarce resources (Jiang et al, 2020). The epicenters of the COVID-19 are challenged with resource problems of shortage of beds, gowns, masks, medical staff, and ventilators (Ahuja et al, 2020; Taiwo and Ezugwu, 2020). We propose the development of a machine learning decision support system to help in crucial decisions on resource allocation.…”

Section: Challenges and Future Research Opportunitiesmentioning

confidence: 99%

Early survey with bibliometric analysis on machine learning approaches in controlling coronavirus

Chiroma

Ezugwu

Jauro

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background and Objective: The COVID-19 pandemic has caused severe mortality across the globe with the USA as the current epicenter, although the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight the COVID-19 pandemic from a different perspective. To the best of the authors knowledge, no comprehensive survey with bibliometric analysis has been conducted on the adoption of machine learning for fighting COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine-learning-based technologies to fight the COVID-19 pandemic from a different perspective, including an extensive systematic literature review and a bibliometric analysis. Methods: A literature survey methodology is applied to retrieve data from academic databases, and a bibliometric technique is subsequently employed to analyze the accessed records. Moreover, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis, and analysis are presented. The convolutional neural network (CNN) is found mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest computed tomography (CT) scan images. Similarly, a bibliometric analysis of machine-learning-based COVID-19-related publications in Scopus and Web of Science citation indexes is performed. Finally, a new perspective is proposed to solve the challenges identified as directions for future research. We believe that the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators. Results: The findings in this study reveal that machine-learning-based COVID-19 diagnostic tools received the most considerable attention from researchers. Specifically, the analyses of the results show that energy and resources are more dispensed toward COVID-19 automated diagnostic tools, while COVID-19 drugs and vaccine development remain grossly underexploited. Moreover, the machine-learning-based algorithm predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images. Conclusions: The challenges hindering practical work on the application of machine-learning-based technologies to fight COVID-19 and a new perspective to solve the identified problems are presented in this study. We believe that the presented survey with bibliometric analysis can help researchers determine areas that need further development and identify potential collaborators at author, country, and institutional levels to advance research in the focused area of machine learning application for disease control.

show abstract

Smart healthcare support for remote patient monitoring during covid-19 quarantine

Cited by 159 publications

References 12 publications

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

iCovidCare: Intelligent health monitoring framework for COVID-19 using ensemble random forest in edge networks

Early survey with bibliometric analysis on machine learning approaches in controlling coronavirus

Contact Info

Product

Resources

About