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Advanced Sensing Technologies in E-health: Trends and Challenges

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

Deadline for manuscript submissions: 15 July 2024 | Viewed by 195

Special Issue Editor


E-Mail Website
Guest Editor
Department of Computer Science, Cardiff Metropolitan University, Cardiff, UK
Interests: Electronic Health Records (EHL); health data analytics; healthcare domain; machine learning; network simulation methods and tools; modelling; communications software engineering; NS2/NAM; formal ontologies

Special Issue Information

Dear Colleagues,

Advanced Sensing Technologies in E-Health is a comprehensive exploration of the dynamic intersection of electronic health (e-health) and the integration of cutting-edge sensing technologies within the healthcare domain. For instance, wearable health devices like smartwatches and fitness trackers serve as prime examples of advanced sensing technologies. These devices continuously monitor vital signs such as heart rate, sleep patterns, and physical activity, delivering real-time data to users and healthcare professionals. Such data enables early detection of anomalies, empowering individuals to take proactive steps to maintain their health. This special issue explores the dynamic landscape of electronic health (e-health) and the integration of cutting-edge sensing technologies into healthcare. This dedicated issue digs in the challenges and opportunities presented by the proliferation of advanced sensors and data-driven healthcare solutions.

Prominent themes in the special issue include the need for data privacy and security in a connected healthcare environment, the potential for wearable and implantable sensors in remote patient monitoring, and the role of artificial intelligence in data analysis and decision-making. It addresses the regulatory hurdles, interoperability concerns, and ethical considerations associated with advanced sensing technologies in healthcare.

The key topics of interest include (but are not limited to):

  • E-Health
  • Healthcare Data
  • Machine learning, deep learning, and big data analytics in E-Health
  • Ethical Considerations
  • Sensing Technologies in E-Health
  • Healthcare Data Privacy
  • Remote Patient Monitoring
  • wearable sensors in healthcare
  • Artificial Intelligence in Healthcare
  • Healthcare Regulatory Challenges
  • Interoperability of Healthcare Data

Dr. Muhammad Azizur Rahman
Guest Editor

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.

Keywords

  • E-Health
  • healthcare data
  • sensing technologies in E-Health

Published Papers (1 paper)

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12 pages, 473 KiB  
Perspective
Feasibility of Observing Cerebrovascular Disease Phenotypes with Smartphone Monitoring: Study Design Considerations for Real-World Studies
by Stephanie J. Zawada, Ali Ganjizadeh, Clint E. Hagen, Bart M. Demaerschalk and Bradley J. Erickson
Sensors 2024, 24(11), 3595; https://doi.org/10.3390/s24113595 (registering DOI) - 2 Jun 2024
Abstract
Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient [...] Read more.
Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient ischemic attack. In this perspective, we present participant feedback and relevant smartphone data metrics suggesting that digital phenotyping of post-stroke depression is feasible. Additionally, we proffer thoughtful considerations for designing feasible real-world study protocols tracking cerebrovascular dysfunction with smartphone sensors. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in E-health: Trends and Challenges)
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