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Internet of Things (IoT) and Wearable Devices Applications in Healthcare...
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Internet of Things (IoT) and Wearable Devices Applications in Healthcare and Disaster Healthcare

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 5628

Special Issue Editors

Dr. Samaneh Madanian

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, Auckland University of Technology, Auckland 1010, New Zealand
Interests: digital health technologies and applications; IoT; AI and data science
Special Issues, Collections and Topics in MDPI journals
Dr. Julie Dugdale

E-Mail Website
Guest Editor
France & Grenoble Informatics Lab (LIG), University Grenoble Alps, 38000 Grenoble, France
Interests: internet of behaviours (IoB); human behaviour modelling; crisis and emergency management
Dr. Mahyar T. Moghaddam

E-Mail Website
Guest Editor
The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5000 Odense, Denmark
Interests: software engineering; cloud computing; Internet of Things (IoT); smart built environments; IoT-based emergency management; self-adaptive software control; internet of behaviours (IoB); sustainability

Special Issue Information

Dear Colleagues,

Due to recent technological enhancements, Internet of Things (IoT) and wearable devices are increasingly being used for wellbeing and healthcare. IoT and wearables enable us to capture and collect longitudinal and real-time data. This makes them extremely valuable for applications in mainstream and disaster healthcare, digital health and telehealth services. IoT utilisation in healthcare promotes health equity and patient empowerment while enhancing the quality of healthcare services. IoT in healthcare, coupled with data analysis, can support monitoring and diagnosis, which leads to predictive and personalised healthcare smart services.      

This Special Issue aims to collect and present original and high-quality papers, both from academic and from industrial players, on novel applications and approaches, new trends, solutions, and challenges of IoT adoption and implementation for healthcare and wellbeing purposes. Reviews and survey papers are also welcomed. Papers are sought on a number of topics, including but not limited to:

  • IoT and wearable devices applications in healthcare;
  • IoT and wearable devices in disaster healthcare;
  • Smart healthcare using IoT and their data analytics;
  • Privacy, security, and safety challenges;
  • Barriers to IoT deployment in healthcare and disaster health;
  • AI and IoT for predictive health services;
  • Analysis of multimodal data, including data overload and accuracy;
  • Monitoring patient/victim mobility in disaster situations;
  • IoT for Pandemic Management;
  • Visualization and Interactive IoT-based systems.

Dr. Samaneh Madanian
Dr. Julie Dugdale
Dr. Mahyar T. Moghaddam
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. Electronics 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 2400 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.

Keywords

  • IoT
  • wearable devices
  • digital health
  • disaster healthcare
  • smart health services
  • IoT data analytics for healthcare
  • interactive IoT-based systems
  • tele-health and tele-mental health
  • pandemic management

Published Papers (1 paper)

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Research

16 pages, 2866 KiB  
Article
Machine-Learning-Based IoT–Edge Computing Healthcare Solutions
by Abdulrahman K. Alnaim and Ahmed M. Alwakeel
Electronics 2023, 12(4), 1027; https://doi.org/10.3390/electronics12041027 - 18 Feb 2023
Cited by 14 | Viewed by 4949
Abstract
The data that medical sensors collect can be overwhelming, making it challenging to glean the most relevant insights. An algorithm for a body sensor network is needed for the purpose of spotting outliers in the collected data. Methods of machine learning and statistical [...] Read more.
The data that medical sensors collect can be overwhelming, making it challenging to glean the most relevant insights. An algorithm for a body sensor network is needed for the purpose of spotting outliers in the collected data. Methods of machine learning and statistical sampling can be used in the research process. Real-time response optimization is a growing field, as more and more computationally intensive tasks are offloaded to the backend. Optimizing data transfers is a topic of study. Computing power is dispersed across many domains. Computation will become a network bottleneck as more and more devices gain Internet-of-Things capabilities. It is crucial to employ both task-level parallelism and distributed computing. To avoid running down the battery, the typical solution is to send the processing to a server in the background. The widespread deployment of Internet-of-Things (IoT) devices has raised serious privacy and security concerns among people everywhere. The rapid expansion of cyber threats has rendered our current privacy and security measures inadequate. Machine learning (ML) methods are gaining popularity because of the reliability of the results that they produce, which can be used to anticipate and detect vulnerabilities in Internet-of-Things-based systems. Network response times are improved by edge computing, which also increases decentralization and security. Edge nodes, which frequently communicate with the cloud, can now handle a sizable portion of mission-critical computation. Real-time, highly efficient solutions are possible with the help of this technology. To this end, we use a distributed-edge-computing-based Internet-of-Things (IoT) framework to investigate how cloud and edge computing can be combined with ML. IoT devices with sensor frameworks can collect massive amounts of data for subsequent analysis. The front-end component can benefit from some forethought in determining what information is most crucial. To accomplish this, an IoT server in the background can offer advice and direction. The idea is to use machine learning in the backend servers to find data signatures of interest. We intend to use the following ideas in the medical field as a case study. Using a distributed-edge-computing-based Internet-of-Things (IoT) framework, we are investigating how to combine the strengths of both cloud and edge computing with those of machine learning. Full article
(This article belongs to the Special Issue Internet of Things (IoT) and Wearable Devices Applications in Healthcare and Disaster Healthcare)
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