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Digital Healthcare in Pandemics
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Digital Healthcare in Pandemics

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (25 October 2021) | Viewed by 14389

Special Issue Editors

Dr. Muhammad Awais

E-Mail Website
Guest Editor
School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK
Interests: digital health; artificial intelligence; applied machine learning; data analytics; biomedical signal processing; Internet of Things (IoT); Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Mohsin Raza

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Applied Sciences (FAS), Edge Hill University, Ormskirk L39 4QP, UK
Interests: Internet of Things (IoT); sensor networks; machine learning; mobile and wireless networks; autonomous transportation systems; Industry 4.0; digital twins
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nik Bessis

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Applied Sciences (FAS), Edge Hill University, Ormskirk L39 4QP, UK
Interests: data analytics; internet of things; smart environments; collective intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The steady increase in healthcare requirements has affected the healthcare infrastructure. The fragility of a sustainable healthcare system has been witnessed frequently in the past few years. While it can be partially attributed to the prevailing health concerns and the emergence of new diseases, lack of legislation and slow-paced adaption of intelligent healthcare systems also pose major challenges.

The recent coronavirus disease 2019 (Covid-19) pandemic has pushed healthcare systems to the verge of collapse. Undoubtedly, the Covid-19 catastrophe has jeopardized human activities at large and exposed us to unseen threats that have damaged the socioeconomic infrastructure. In such circumstances, smart healthcare offers a sustainable future where technology-driven solutions founded in artificial intelligence, information and communications technology (ICT), cloud and fog computing, decision support systems, big data analytics, telehealth, transportation and emergency response, robotics, assistive technologies, distributed processes, remote monitoring, disease diagnosis, and internet of medical things (IoMT) can have a substantial impact. This Special Issue focuses on both theoretical and practical aspects in digital healthcare and the provision of smart healthcare solutions amidst pandemics.

Topics of interest include, but are not limited to:

  • Internet of Medical Things (IoMT);
  • Developing and building new models and architecture for IoMT;
  • Decision support systems for IoMT-enabled healthcare in pandemics and health crises;
  • IoMT for emergency services and pandemics;
  • Performance, scalability, and reliability in IoMT;
  • Cloud-based healthcare solutions in pandemics;
  • Body area networks;
  • Security and privacy;
  • Intelligent systems;
  • Machine learning approaches to facilitate diagnosis and prediction;
  • Deep learning for predictive modelling and the realization of digital twins in healthcare;
  • Medical image analysis;
  • Medical/bio sensors and sensor networks;
  • Telehealth solutions and digital doctors;
  • Digital twins in healthcare infrastructure;
  • Predictive request routing and medical resource allocation;
  • Deep learning techniques for diagnosis and decision support;
  • Intelligent transportation to facilitate the provision of healthcare services and emergency response;
  • ICT-enabled assistive technologies;
  • Cloud and fog computing in digital health realization;
  • IoMT for the remote monitoring of patients.

You may choose our Joint Special Issue in Digital.

Dr. Muhammad Awais
Prof. Dr. Mohsin Raza
Prof. Dr. Nik Bessis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

12 pages, 22346 KiB  
Article
Standard for the Quantification of a Sterilization Effect Using an Artificial Intelligence Disinfection Robot
by Heeju Hong, WonKook Shin, Jieun Oh, SunWoo Lee, TaeYoung Kim, WooSub Lee, JongSuk Choi, SeungBeum Suh and KangGeon Kim
Sensors 2021, 21(23), 7776; https://doi.org/10.3390/s21237776 - 23 Nov 2021
Cited by 9 | Viewed by 4076
Abstract
Recent outbreaks and the worldwide spread of COVID-19 have challenged mankind with unprecedented difficulties. The introduction of autonomous disinfection robots appears to be indispensable as consistent sterilization is in desperate demand under limited manpower. In this study, we developed an autonomous navigation robot [...] Read more.
Recent outbreaks and the worldwide spread of COVID-19 have challenged mankind with unprecedented difficulties. The introduction of autonomous disinfection robots appears to be indispensable as consistent sterilization is in desperate demand under limited manpower. In this study, we developed an autonomous navigation robot capable of recognizing objects and locations with a high probability of contamination and capable of providing quantified sterilization effects. In order to quantify the 99.9% sterilization effect of various bacterial strains, as representative contaminants with robots operated under different modules, the operating parameters of the moving speed, distance between the sample and the robot, and the radiation angle were determined. We anticipate that the sterilization effect data we obtained with our disinfection robot, to the best of our knowledge, for the first time, will serve as a type of stepping stone, leading to practical applications at various sites requiring disinfection. Full article
(This article belongs to the Special Issue Digital Healthcare in Pandemics)
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22 pages, 12659 KiB  
Article
Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset
by Muhammad Umair, Muhammad Shahbaz Khan, Fawad Ahmed, Fatmah Baothman, Fehaid Alqahtani, Muhammad Alian and Jawad Ahmad
Sensors 2021, 21(17), 5813; https://doi.org/10.3390/s21175813 - 29 Aug 2021
Cited by 35 | Viewed by 6194
Abstract
The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it [...] Read more.
The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction. Full article
(This article belongs to the Special Issue Digital Healthcare in Pandemics)
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17 pages, 8352 KiB  
Article
Future Forecasting of COVID-19: A Supervised Learning Approach
by Mujeeb Ur Rehman, Arslan Shafique, Sohail Khalid, Maha Driss and Saeed Rubaiee
Sensors 2021, 21(10), 3322; https://doi.org/10.3390/s21103322 - 11 May 2021
Cited by 19 | Viewed by 2943
Abstract
A little over a year after the official announcement from the WHO, the COVID-19 pandemic has led to dramatic consequences globally. Today, millions of doses of vaccines have already been administered in several countries. However, the positive effect of these vaccines will probably [...] Read more.
A little over a year after the official announcement from the WHO, the COVID-19 pandemic has led to dramatic consequences globally. Today, millions of doses of vaccines have already been administered in several countries. However, the positive effect of these vaccines will probably be seen later than expected. In these circumstances, the rapid diagnosis of COVID-19 still remains the only way to slow the spread of this virus. However, it is difficult to predict whether a person is infected or not by COVID-19 while relying only on apparent symptoms. In this context, we propose to use machine learning (ML) algorithms in order to diagnose COVID-19 infected patients more effectively. The proposed diagnosis method takes into consideration several symptoms, such as flu symptoms, throat pain, immunity status, diarrhea, voice type, body temperature, joint pain, dry cough, vomiting, breathing problems, headache, and chest pain. Based on these symptoms that are modelled as ML features, our proposed method is able to predict the probability of contamination with the COVID-19 virus. This method is evaluated using different experimental analysis metrics such as accuracy, precision, recall, and F1-score. The obtained experimental results have shown that the proposed method can predict the presence of COVID-19 with over 97% accuracy. Full article
(This article belongs to the Special Issue Digital Healthcare in Pandemics)
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