Special Issue Computational Intelligence for Physiological Sensors and Body Sensor Network
CIPSBSN 2020
- URL: https://www.mdpi.com/journal/electronics/special_issues/Comput_Intelligence_Sensors
- Call For Paper Type: SI
- H2 Index: 0
- Submission Date: 2020-02-29
Electrochemistry Microelectronics & Electronic Packaging Remote Sensing Electromagnetism
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, Kaunas University of Technology, Kaunas, Lithuania
Guest Editor
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