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Biomedical Data Analysis and Sensing in Human-Machine Interaction: 2nd Edition
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Biomedical Data Analysis and Sensing in Human-Machine Interaction: 2nd Edition

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

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 8626

Special Issue Editors

Prof. Dr. Aleksandra Kawala-Sterniuk

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
Interests: analysis of biomedical data; human–computer interactions; brain–computer interfaces and the use of modern technologies in the diagnosis of neurodegenerative diseases
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Waldemar Karwowski

E-Mail Website
Guest Editor
Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
Interests: human systems integration; ergonomics and safety; systems engineering; human-computer interaction; fuzzy logic and neuro-fuzzy modeling; complex systems; nonlinear dynamics in human performance; neuroergonomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is volume II of our Special Issue, the primary objective of which is to present the most current advancements in the analysis of biomedical data and the exploration of these methods’ potential in human–computer interaction. This Special Issue will cover the latest techniques applied for various types of biomedical data processing, such as real and model-based biosignals analysis, data integration, and medical decision-making support, among others. It will also focus on the most current sensing technologies applied for biosignals acquisition. Analysis of biomedical data spans a broad spectrum of intriguing topics, and this Special Issue aims to comprehensively address most of them.

The submitted papers should cover the following areas:

  1. Analysis of biomedical data;
  2. Industry 4.0/Industry 5.0;
  3. Smart homes/cities;
  4. Brain–computer interfaces;
  5. Human–machine/computer interaction;
  6. Graphomotorics;
  7. Neurodegenerative disorders—diagnostics support;
  8. Movement disorders;
  9. Virtual reality;
  10. Sensors—modern sensing technologies;
  11. Internet of Things;
  12. Biocybernetics.

Other topics could be also taken into account for publication. We also welcome concept and review papers.

Dr. Aleksandra Kawala-Sterniuk
Prof. Dr. Waldemar Karwowski
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.

Keywords

  • biomedical data
  • signal processing
  • data analysis
  • human–computer interfaces
  • human–machine interaction
  • modern sensing technologies

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Related Special Issue

Published Papers (2 papers)

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Research

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17 pages, 696 KiB  
Article
Viewer Engagement in Response to Mixed and Uniform Emotional Content in Marketing Videos—An Electroencephalographic Study
by Izabela Rejer, Jarosław Jankowski, Justyna Dreger and Krzysztof Lorenz
Sensors 2024, 24(2), 517; https://doi.org/10.3390/s24020517 - 14 Jan 2024
Cited by 3 | Viewed by 2546
Abstract
This study presents the results of an experiment designed to investigate whether marketing videos containing mixed emotional content can sustain consumers interest longer compared to videos conveying a consistent emotional message. During the experiment, thirteen participants, wearing EEG (electroencephalographic) caps, were exposed to [...] Read more.
This study presents the results of an experiment designed to investigate whether marketing videos containing mixed emotional content can sustain consumers interest longer compared to videos conveying a consistent emotional message. During the experiment, thirteen participants, wearing EEG (electroencephalographic) caps, were exposed to eight marketing videos with diverse emotional tones. Participant engagement was measured with an engagement index, a metric derived from the power of brain activity recorded over the frontal and parietal cortex and computed within three distinct frequency bands: theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz). The outcomes indicated a statistically significant influence of emotional content type (mixed vs. consistent) on the duration of user engagement. Videos containing a mixed emotional message were notably more effective in sustaining user engagement, whereas the engagement level for videos with a consistent emotional message declined over time. The principal inference drawn from the study is that advertising materials conveying a consistent emotional message should be notably briefer than those featuring a mixed emotional message to achieve an equivalent level of message effectiveness, measured through engagement duration. Full article
(This article belongs to the Special Issue Biomedical Data Analysis and Sensing in Human-Machine Interaction: 2nd Edition)
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Review

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31 pages, 766 KiB  
Review
A Review of Patient Bed Sensors for Monitoring of Vital Signs
by Michaela Recmanik, Radek Martinek, Jan Nedoma, Rene Jaros, Mariusz Pelc, Radovan Hajovsky, Jan Velicka, Martin Pies, Marta Sevcakova and Aleksandra Kawala-Sterniuk
Sensors 2024, 24(15), 4767; https://doi.org/10.3390/s24154767 - 23 Jul 2024
Cited by 2 | Viewed by 5267
Abstract
The analysis of biomedical signals is a very challenging task. This review paper is focused on the presentation of various methods where biomedical data, in particular vital signs, could be monitored using sensors mounted to beds. The presented methods to monitor vital signs [...] Read more.
The analysis of biomedical signals is a very challenging task. This review paper is focused on the presentation of various methods where biomedical data, in particular vital signs, could be monitored using sensors mounted to beds. The presented methods to monitor vital signs include those combined with optical fibers, camera systems, pressure sensors, or other sensors, which may provide more efficient patient bed monitoring results. This work also covers the aspects of interference occurrence in the above-mentioned signals and sleep quality monitoring, which play a very important role in the analysis of biomedical signals and the choice of appropriate signal-processing methods. The provided information will help various researchers to understand the importance of vital sign monitoring and will be a thorough and up-to-date summary of these methods. It will also be a foundation for further enhancement of these methods. Full article
(This article belongs to the Special Issue Biomedical Data Analysis and Sensing in Human-Machine Interaction: 2nd Edition)
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