Special Issue "Computational Intelligence for Physiological Sensors and Body Sensor Networks"
Deadline for manuscript submissions: closed (15 July 2021) | Viewed by 34560
Interests: disease diagnostics using artificial intelligence methods
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The rapid development of electronics leads to the applications in many areas of science and technology, whilst simultaneously creating many challenging problems in every aspect of modern life. A body sensor network (BSN) connects sensors and devices that are placed around the human body or in personal clothing to collect physiological data. Different sensor technologies are used to collect this data, like physiological sensors (e.g., EEG, ECG, electrodermal activity, and skin conductance) and other non-intrusive sensors and devices (e.g., imaging cameras, Leap Motion, and Kinect). The collected data must be analyzed using intelligent methods in order to be usable in a variety of applications such as ambient assisted living, health monitoring, rehabilitation, sports, emotion-aware intelligent systems, and gaming.
Current research is interdisciplinary in nature, reflecting a combination of concepts and methods that often span several areas of electrical engineering, mathematics, health sciences, and other scientific disciplines. In these areas of application, the use of computational intelligence methods to enhance the efficiency and quality of results is very important. Contributions covering neural networks, bioinspired methods, and other computational intelligence methods are welcomed.
This Special Issue invites contributions that address (i) sensing technologies and issues and (ii) computational intelligence techniques of relevance to tackle the challenges above. In particular, submitted papers should clearly show novel contributions and innovative applications covering but not limited to any of the following topics:
- ECG, EEG, and electrodermal activity sensor systems;
- Sensor data pre-processing, noise filtering, and calibration concepts for physiological signals;
- Electrical circuits and devices for wearable electronics;
- Deep learning and bioinspired algorithms for a better understanding of BSN-collected physiological signals;
- Applications in BSNs for human activity recognition, sport monitoring, emotion recognition, and health care.
Technical Program Committee Members:
- Dr. Marcin Wozniak, Silesian University of Technology, Gliwice, Poland
- Prof. Dr. Victor Hugo C. de Albuquerque, Universidade de Fortaleza, Fortaleza, Brazil
- Dr. Wei Wei, Xi'an University of Technology, Xi'an, China
Prof. Dr. Robertas Damaševičius
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 2200 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.
- Computational intelligence
- Digital signal processing
- Wireless sensor networks
- Biomedical electronics
- Wearable electronics
- Body area networks
- Internet of Things
- Health monitoring