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Towards Improved Health using Digital Sensors in an Environmental Monitoring System

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 2337

Special Issue Editors

Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Muehlenpfordtstr. 23, D-38106 Braunschweig, Lower Saxony, Germany
Interests: sensor technology; accident and emergency informatics; Ambient Assisted Living (AAL); data science in medicine; patient safety; health information systems

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Guest Editor
Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig, Hannover Medical School, Braunschweig, Germany
Interests: wearable devices; hardware embedded systems; smart medical homes; Ambient Assisted Living (AAL); environmental measurement; IoT, and telehealth monitoring

Special Issue Information

Dear Colleagues,

We are pleased to announce a new Special Issue, Towards Improved Health using Digital Sensors in an Environmental Monitoring System.

Sensor-enhanced environmental monitoring systems (EMS) can measure, monitor, and record various physical and chemical parameters, including location, air pressure, temperature, humidity, gas concentration, particulate matter (PM), dust, ultraviolet (UV), sound level, and other contextual elements. The use of digital sensors in environmental monitoring has become more prevalent, as they facilitate the transfer of data, data storage, and system interoperability. This enables shifting and spreading the EMS from stationary forecasting systems in metropolitan areas to portable systems for indoor air quality monitoring and wearable devices in the field of personalized healthcare. Furthermore, recent advances in paradigms such as the Internet of Things (IoT), artificial intelligence (AI), 5G, big data, and smart cities allow the implementation of highly accurate and robust environmental monitoring infrastructures.

In this Special Issue, we aim to provide scientists and engineers with innovative research findings on developing and applying digital sensors in environmental monitoring to achieve the goal of improved health. We welcome original research, visionary articles, and survey/review articles. Possible topics include but are not limited to the following subjects:

  • Hardware design;
  • Material science in digital sensors;
  • Sensor calibration;
  • Sensor network, IoT, and 5G;
  • Signal processing;
  • Data fusion;
  • Machine learning/deep learning;
  • Data management;
  • System interoperability;
  • System integration;
  • System security.

We look forward to your valuable contributions!

Dr. Ju Wang
Dr. Mostafa Haghi 
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 (1 paper)

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Research

29 pages, 7159 KiB  
Article
Real-Time Learning and Monitoring System in Fighting against SARS-CoV-2 in a Private Indoor Environment
by Serdar Erişen
Sensors 2022, 22(18), 7001; https://doi.org/10.3390/s22187001 - 15 Sep 2022
Cited by 6 | Viewed by 1760
Abstract
The SARS-CoV-2 virus has posed formidable challenges that must be tackled through scientific and technological investigations on each environmental scale. This research aims to learn and report about the current state of user activities, in real-time, in a specially designed private indoor environment [...] Read more.
The SARS-CoV-2 virus has posed formidable challenges that must be tackled through scientific and technological investigations on each environmental scale. This research aims to learn and report about the current state of user activities, in real-time, in a specially designed private indoor environment with sensors in infection transmission control of SARS-CoV-2. Thus, a real-time learning system that evolves and updates with each incoming piece of data from the environment is developed to predict user activities categorized for remote monitoring. Accordingly, various experiments are conducted in the private indoor space. Multiple sensors, with their inputs, are analyzed through the experiments. The experiment environment, installed with microgrids and Internet of Things (IoT) devices, has provided correlating data of various sensors from that special care context during the pandemic. The data is applied to classify user activities and develop a real-time learning and monitoring system to predict the IoT data. The microgrids were operated with the real-time learning system developed by comprehensive experiments on classification learning, regression learning, Error-Correcting Output Codes (ECOC), and deep learning models. With the help of machine learning experiments, data optimization, and the multilayered-tandem organization of the developed neural networks, the efficiency of this real-time monitoring system increases in learning the activity of users and predicting their actions, which are reported as feedback on the monitoring interfaces. The developed learning system predicts the real-time IoT data, accurately, in less than 5 milliseconds and generates big data that can be deployed for different usages in larger-scale facilities, networks, and e-health services. Full article
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