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Electronics
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Computational Intelligence for Physiological Sensors and Body Sensor Networks
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Computational Intelligence for Physiological Sensors and Body Sensor Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (15 July 2021) | Viewed by 38826

Special Issue Editor

Prof. Dr. Robertas Damaševičius

E-Mail Website
Guest Editor
Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: disease diagnostics using artificial intelligence methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

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
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 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 2400 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

  • Computational intelligence
  • Digital signal processing
  • Wireless sensor networks
  • Biomedical electronics
  • Wearable electronics
  • Body area networks
  • Biomonitoring
  • Internet of Things
  • Health monitoring
  • Sensors.

Published Papers (6 papers)

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Research

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15 pages, 3842 KiB  
Article
GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals
by Hui Wen Loh, Chui Ping Ooi, Elizabeth Palmer, Prabal Datta Barua, Sengul Dogan, Turker Tuncer, Mehmet Baygin and U. Rajendra Acharya
Electronics 2021, 10(14), 1740; https://doi.org/10.3390/electronics10141740 - 20 Jul 2021
Cited by 50 | Viewed by 5301
Abstract
Parkinson’s disease (PD) is globally the most common neurodegenerative movement disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the brain. However, current methods to diagnose PD on the basis of clinical features of Parkinsonism may lead [...] Read more.
Parkinson’s disease (PD) is globally the most common neurodegenerative movement disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the brain. However, current methods to diagnose PD on the basis of clinical features of Parkinsonism may lead to misdiagnoses. Hence, noninvasive methods such as electroencephalographic (EEG) recordings of PD patients can be an alternative biomarker. In this study, a deep-learning model is proposed for automated PD diagnosis. EEG recordings of 16 healthy controls and 15 PD patients were used for analysis. Using Gabor transform, EEG recordings were converted into spectrograms, which were used to train the proposed two-dimensional convolutional neural network (2D-CNN) model. As a result, the proposed model achieved high classification accuracy of 99.46% (±0.73) for 3-class classification (healthy controls, and PD patients with and without medication) using tenfold cross-validation. This indicates the potential of proposed model to simultaneously automatically detect PD patients and their medication status. The proposed model is ready to be validated with a larger database before implementation as a computer-aided diagnostic (CAD) tool for clinical-decision support. Full article
(This article belongs to the Special Issue Computational Intelligence for Physiological Sensors and Body Sensor Networks)
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20 pages, 783 KiB  
Article
Weighted Random Forests to Improve Arrhythmia Classification
by Krzysztof Gajowniczek, Iga Grzegorczyk, Tomasz Ząbkowski and Chandrajit Bajaj
Electronics 2020, 9(1), 99; https://doi.org/10.3390/electronics9010099 - 03 Jan 2020
Cited by 25 | Viewed by 3637
Abstract
Construction of an ensemble model is a process of combining many diverse base predictive learners. It arises questions of how to weight each model and how to tune the parameters of the weighting process. The most straightforward approach is simply to average the [...] Read more.
Construction of an ensemble model is a process of combining many diverse base predictive learners. It arises questions of how to weight each model and how to tune the parameters of the weighting process. The most straightforward approach is simply to average the base models. However, numerous studies have shown that a weighted ensemble can provide superior prediction results to a simple average of models. The main goals of this article are to propose a new weighting algorithm applicable for each tree in the Random Forest model and the comprehensive examination of the optimal parameter tuning. Importantly, the approach is motivated by its flexibility, good performance, stability, and resistance to overfitting. The proposed scheme is examined and evaluated on the Physionet/Computing in Cardiology Challenge 2015 data set. It consists of signals (electrocardiograms and pulsatory waveforms) from intensive care patients which triggered an alarm for five cardiac arrhythmia types (Asystole, Bradycardia, Tachycardia, Ventricular Tachycardia, and Ventricular Fultter/Fibrillation). The classification problem regards whether the alarm should or should not have been generated. It was proved that the proposed weighting approach improved classification accuracy for the three most challenging out of the five investigated arrhythmias comparing to the standard Random Forest model. Full article
(This article belongs to the Special Issue Computational Intelligence for Physiological Sensors and Body Sensor Networks)
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19 pages, 4851 KiB  
Article
Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals
by Justas Šalkevicius, Robertas Damaševičius, Rytis Maskeliunas and Ilona Laukienė
Electronics 2019, 8(9), 1039; https://doi.org/10.3390/electronics8091039 - 16 Sep 2019
Cited by 91 | Viewed by 15664
Abstract
Virtual reality exposure therapy (VRET) can have a significant impact towards assessing and potentially treating various anxiety disorders. One of the main strengths of VRET systems is that they provide an opportunity for a psychologist to interact with virtual 3D environments and change [...] Read more.
Virtual reality exposure therapy (VRET) can have a significant impact towards assessing and potentially treating various anxiety disorders. One of the main strengths of VRET systems is that they provide an opportunity for a psychologist to interact with virtual 3D environments and change therapy scenarios according to the individual patient’s needs. However, to do this efficiently the patient’s anxiety level should be tracked throughout the VRET session. Therefore, in order to fully use all advantages provided by the VRET system, a mental stress detection system is needed. The patient’s physiological signals can be collected with wearable biofeedback sensors. Signals like blood volume pressure (BVP), galvanic skin response (GSR), and skin temperature can be processed and used to train the anxiety level classification models. In this paper, we combine VRET with mental stress detection and highlight potential uses of this kind of VRET system. We discuss and present a framework for anxiety level recognition, which is a part of our developed cloud-based VRET system. Physiological signals of 30 participants were collected during VRET-based public speaking anxiety treatment sessions. The acquired data were used to train a four-level anxiety recognition model (where each level of ‘low’, ‘mild’, ‘moderate’, and ‘high’ refer to the levels of anxiety rather than to separate classes of the anxiety disorder). We achieved an 80.1% cross-subject accuracy (using leave-one-subject-out cross-validation) and 86.3% accuracy (using 10 × 10 fold cross-validation) with the signal fusion-based support vector machine (SVM) classifier. Full article
(This article belongs to the Special Issue Computational Intelligence for Physiological Sensors and Body Sensor Networks)
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12 pages, 3360 KiB  
Article
Utilizing HRV-Derived Respiration Measures for Driver Drowsiness Detection
by Jinwoo Kim and Miyoung Shin
Electronics 2019, 8(6), 669; https://doi.org/10.3390/electronics8060669 - 13 Jun 2019
Cited by 19 | Viewed by 3621
Abstract
This study aims to utilize heart rate variability (HRV) signals obtained with a wearable sensor for driver drowsiness detection. To this end, we investigated respiration characteristics derived from HRV signals based on the known fact that respiratory activity can be estimated from the [...] Read more.
This study aims to utilize heart rate variability (HRV) signals obtained with a wearable sensor for driver drowsiness detection. To this end, we investigated respiration characteristics derived from HRV signals based on the known fact that respiratory activity can be estimated from the high frequency (HF) band of HRV signals. For drowsiness detection, many earlier works commonly used dominant respiration (DR) characteristics. However, in some situations where emphasized power in a power spectrum of HRV occurs at multi sub-frequency, the DR measures may possibly fail to capture overall respiration characteristics. To handle this problem, we propose two spectral indices, the weighted mean (WM) and the weighted standard deviation (WSD) of the HF band in the power spectrum. These indices are used to properly capture the overall shape of the respiratory activity shown through the HF band of the HRV power spectrum as an alternative to the DR measures. For experiments, we collected HRV data with an electrocardiogram device worn on the body under a virtual driving environment. The proposed indices somewhat clearly showed the tendency that respiratory frequency decreases and respiration regularity increases in drowsy states of all subjects, while existing DR measures hardly showed this. In addition, when the proposed indices are used alone or together with conventional HRV-related measures as input features for classification models, they showed the best performance in distinguishing drowsiness from wakefulness. Full article
(This article belongs to the Special Issue Computational Intelligence for Physiological Sensors and Body Sensor Networks)
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Review

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29 pages, 485 KiB  
Review
Innovative Use of Wrist-Worn Wearable Devices in the Sports Domain: A Systematic Review
by Juan M. Santos-Gago, Mateo Ramos-Merino, Sonia Vallarades-Rodriguez, Luis M. Álvarez-Sabucedo, Manuel J. Fernández-Iglesias and Jose L. García-Soidán
Electronics 2019, 8(11), 1257; https://doi.org/10.3390/electronics8111257 - 01 Nov 2019
Cited by 21 | Viewed by 5663
Abstract
Wrist wearables are becoming more and more popular, and its use is widespread in sports, both professional and amateur. However, at present, they do not seem to exploit all their potential. The objective of this study is to explore innovative proposals for the [...] Read more.
Wrist wearables are becoming more and more popular, and its use is widespread in sports, both professional and amateur. However, at present, they do not seem to exploit all their potential. The objective of this study is to explore innovative proposals for the use of wearable wrist technology in the field of sports, to understand its potential and identify new challenges and lines of future research related to this technology. A systematic review of the scientific literature, collected in 4 major repositories, was carried out to locate research initiatives where wrist wearables were introduced to address some sports-related challenges. Those works that were limited to evaluating sensor performance in sports activities and those in which wrist wearable devices did not play a significant role were excluded. 26 articles were eventually selected for full-text analysis that discuss the introduction of wrist-worn wearables to address some innovative use in the sports field. This study showcases relevant proposals in 10 different sports. The research initiatives identified are oriented to the use of wearable wrist technology (i) for the comprehensive monitoring of sportspeople’s behavior in activities not supported by the vendors, (ii) to identify specific types of movements or actions in specific sports, and (iii) to prevent injuries. There are, however, open issues that should be tackled in the future, such as the incorporation of these devices in sports activities not currently addressed, or the provision of specific recommendation services for sport practitioners. Full article
(This article belongs to the Special Issue Computational Intelligence for Physiological Sensors and Body Sensor Networks)
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28 pages, 13645 KiB  
Review
Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review
by Bardia Yousefi and Chu Kiong Loo
Electronics 2019, 8(10), 1169; https://doi.org/10.3390/electronics8101169 - 15 Oct 2019
Cited by 5 | Viewed by 3505
Abstract
Theoretical neuroscience investigation shows valuable information on the mechanism for recognizing the biological movements in the mammalian visual system. This involves many different fields of researches such as psychological, neurophysiology, neuro-psychological, computer vision, and artificial intelligence (AI). The research on these areas provided [...] Read more.
Theoretical neuroscience investigation shows valuable information on the mechanism for recognizing the biological movements in the mammalian visual system. This involves many different fields of researches such as psychological, neurophysiology, neuro-psychological, computer vision, and artificial intelligence (AI). The research on these areas provided massive information and plausible computational models. Here, a review on this subject is presented. This paper describes different perspective to look at this task including action perception, computational and knowledge based modeling, psychological, and neuroscience approaches. Full article
(This article belongs to the Special Issue Computational Intelligence for Physiological Sensors and Body Sensor Networks)
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