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Special Issue "Sensors for Medical and Industrial Applications"

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

Deadline for manuscript submissions: 15 June 2020.

Special Issue Editor

Dr. Natividad Duro Carralero
E-Mail Website
Guest Editor
Department of Computer Sciences and Automatic Control, UNED, C/Juan del Rosal, 16, 28040 Madrid, Spain
Interests: sensor data fusion; IoT environments; industry applications; machine learning; big data; deep learning
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

At present, medical and industrial applications use many different sensors, because it is increasingly common to collect as much information as possible from our systems. New technologies allow us to analyze these data and obtain relevant information from them. From this analysis, it is possible to support important decision making that leads to higher productivity and more efficient operations.

In all these applications, we can collect the information from physical sensors, but it is also very common to connect some sensors of our systems to the Internet. The Internet of Things (IoT) represents a revolution but also a real trend in the software industry. The use of the IoT implies a fusion between the digital and the physical worlds, involving sensors that are embedded in all kinds of devices interconnected between them and increasing the amount of data to analyze. To analyze these large volumes of data from multiple sources, we need to use special mathematical methods, algorithms, and techniques which help us to understand these data.

This Special Issue encourages authors from academia, medicine, and industry to submit new research results from the use of multiple sensors in applications of these areas. The sensors can be directly connected to the system, but they can also be connected to the Internet. The topics of this Special Issue will include, but are not limited to:

  • Sensors for medical applications;
  • IoT environments for medical applications;
  • Sensors for industrial applications;
  • IoT environments for industrial applications;
  • Mathematical algorithms and techniques to analyze medical sensor data;
  • Mathematical algorithms and techniques to analyze industrial sensor data.

Dr. Natividad Duro Carralero
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 papers will be 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 2000 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

  • sensors;
  • medical applications;
  • industrial applications;
  • IoT environment;
  • mathematical algorithms for data fusion

Published Papers (1 paper)

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Research

Open AccessArticle
An Oximetry Based Wireless Device for Sleep Apnea Detection
Sensors 2020, 20(3), 888; https://doi.org/10.3390/s20030888 - 07 Feb 2020
Abstract
Sleep related disorders can severely disturb the quality of sleep. Among these disorders, obstructive sleep apnea (OSA) is highly prevalent and commonly undiagnosed. Polysomnography is considered to be the gold standard exam for OSA diagnosis. Even though this multi-parametric test provides highly accurate [...] Read more.
Sleep related disorders can severely disturb the quality of sleep. Among these disorders, obstructive sleep apnea (OSA) is highly prevalent and commonly undiagnosed. Polysomnography is considered to be the gold standard exam for OSA diagnosis. Even though this multi-parametric test provides highly accurate results, it is time consuming, labor-intensive, and expensive. A non-invasive and easy to self-assemble home monitoring device was developed to address these issues. The device can perform the OSA diagnosis at the patient’s home and a specialized technician is not required to supervise the process. An automatic scoring algorithm was developed to examine the blood oxygen saturation signal for a minute-by-minute OSA assessment. It was performed by analyzing statistical and frequency-based features that were fed to a classifier. Afterward, the ratio of the number of minutes classified as OSA to the time in bed in minutes was compared with a threshold for the global (subject-based) OSA diagnosis. The average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve for the minute-by-minute assessment were, respectively, 88%, 80%, 91%, and 0.86. The subject-based accuracy was 95%. The performance is in the same range as the best state of the art methods for the models based only on the blood oxygen saturation analysis. Therefore, the developed model has the potential to be employed in clinical analysis. Full article
(This article belongs to the Special Issue Sensors for Medical and Industrial Applications)
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