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Wireless Body Area Networks (WBAN)
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Wireless Body Area Networks (WBAN)

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 11760

Special Issue Editors

Dr. Daluwathu Mulla Gamage Preethichandra

E-Mail Website
Guest Editor
School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4700, Australia
Interests: sensors; sensor networks; mechatronics; biomedical engineering
Prof. Chandrathilak de Silva Liyanage

E-Mail Website
Guest Editor
School of Digital Science, University of Brunei Darussalam, Gadong, Brunei
Interests: Signal Processing (Speech & Image); Internet Of Things (IoT); Sensor Integration; Power System Analysis
Dr. Umer Izhar

E-Mail Website
Guest Editor
School of Science Technology and Engineering, University of the Sunshine Coast, Sunshine Coast, QLD 4556, Australia
Interests: Mechatronics/Micro Mechatronics; Micro Electromechanical Systems (MEMS); Robotics and Automation
Dr. Lasi Piyathilaka

E-Mail Website
Guest Editor
School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4700, Australia
Interests: Electrical and Electronic Engineering - Control Systems, Robotics and Automation; Electrical and Electronic Engineering - Industrial Electronics; Mechanical Engineering - Automation and Control Engineering

Special Issue Information

Dear Colleagues,

Wireless body area networks have become a popular area of research in the recent past with the availability of wireless wearable IoT devices. The use of wireless body area networks has enriched biomedical data collection in the health, sports, biomedical, security, and healthcare fields. Wireless IoT devices have become very common and affordable now, and this has enabled researchers to develop wireless body area sensor networks for various applications. Motion sensing in real-time for sports and exercise together with measurement of biomedical parameters of a player while playing without interfering with sport activity play a great role in the training and performance enhancement of players. Monitoring biomedical and motion information of residents through WBAN in aged care facilities will provide continuous data on individual residents. This enables personalized care, and it will improve the quality of service. Telemedicine is another promising area of WBAN application which could help to monitor patients’ biomedical parameters remotely, especially when the health system cannot support hospitalization in the context of a pandemic.

Authors are invited to submit original high-quality papers reporting novel advances in wireless body area networks, including but not limited to the following topics:

  • Wearable health monitoring;
  • WBAN addressing and routing protocols;
  • Energy harvesting for WBAN applications;
  • Energy-efficient physical and Mac layer protocols for WBAN;
  • Edge computing for WBAN;
  • Healthcare applications of WBAN;
  • WBAN in patient condition monitoring;
  • Accurate motion detection of the human/animal body;
  • Privacy and ethical issues related to WBAN;
  • Medical application of WBAN;
  • Security of data in WBAN;
  • New hardware and software architectures for WBAN;
  • WBAN in aged care and assisted living;
  • WBAN in sports applications;
  • WBAN for industrial safety;
  • WBAN applications in telemedicine;
  • COVID-19-related applications of WBAN.

Prof. Dr. Daluwathu Mulla Gamage Preethichandra
Prof. Chandrathilak de Silva Liyanage
Dr. Umer Izhar
Dr. Lasi Piyathilaka
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

  • body area networks
  • wireless sensor networks
  • biomedical sensors
  • wearable sensors

Published Papers (5 papers)

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Research

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27 pages, 2963 KiB  
Article
User Orientation Detection in Relation to Antenna Geometry in Ultra-Wideband Wireless Body Area Networks Using Deep Learning
by Sebastian Urwan and Krzysztof K. Cwalina
Sensors 2024, 24(7), 2060; https://0-doi-org.brum.beds.ac.uk/10.3390/s24072060 - 23 Mar 2024
Viewed by 429
Abstract
In this paper, the issue of detecting a user’s position in relation to the antenna geometry in ultra-wideband (UWB) off-body wireless body area network (WBAN) communication using deep learning methods is presented. To measure the impulse response of the channel, a measurement stand [...] Read more.
In this paper, the issue of detecting a user’s position in relation to the antenna geometry in ultra-wideband (UWB) off-body wireless body area network (WBAN) communication using deep learning methods is presented. To measure the impulse response of the channel, a measurement stand consisting of EVB1000 devices and DW1000 radio modules was developed and indoor static measurement scenarios were performed. It was proven that for the binary classification of user orientation, neural networks achieved accuracy that was more than 9% higher than that for the well-known threshold method. In addition, the classification of user position angles relative to the reference node was analyzed. It was proven that, using the proposed deep learning approach and the channel impulse response, it was possible to estimate the angle of the user’s position in relation to the antenna geometry. Absolute user orientation angle errors of about 4–7° for convolutional neural networks and of about 14–15° for multilayer perceptrons were achieved in approximately 85% of the cases in both tested scenarios. Full article
(This article belongs to the Special Issue Wireless Body Area Networks (WBAN))
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15 pages, 6127 KiB  
Article
Predicting Injuries in Football Based on Data Collected from GPS-Based Wearable Sensors
by Tomasz Piłka, Bartłomiej Grzelak, Aleksandra Sadurska, Tomasz Górecki and Krzysztof Dyczkowski
Sensors 2023, 23(3), 1227; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031227 - 20 Jan 2023
Cited by 5 | Viewed by 5423
Abstract
The growing intensity and frequency of matches in professional football leagues are related to the increasing physical player load. An incorrect training model results in over- or undertraining, which is related to a raised probability of an injury. This research focuses on predicting [...] Read more.
The growing intensity and frequency of matches in professional football leagues are related to the increasing physical player load. An incorrect training model results in over- or undertraining, which is related to a raised probability of an injury. This research focuses on predicting non-contact lower body injuries coming from over- or undertraining. The purpose of this analysis was to create decision-making models based on data collected during both training and match, which will enable the preparation of a tool to model the load and report the increased risk of injury for a given player in the upcoming microcycle. For this purpose, three decision-making methods were implemented. Rule-based and fuzzy rule-based methods were prepared based on expert understanding. As a machine learning baseline XGBoost algorithm was considered. Taking into account the dataset used containing parameters related to the external load of the player, it is possible to predict the risk of injury with a certain precision, depending on the method used. The most promising results were achieved by the machine learning method XGBoost algorithm (Precision 92.4%, Recall 96.5%, and F1-score 94.4%). Full article
(This article belongs to the Special Issue Wireless Body Area Networks (WBAN))
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14 pages, 20294 KiB  
Article
SkiMon: A Wireless Body Area Network for Monitoring Ski Flex and Motion during Skiing Sports
by Aaron S. Crandall, Steven Mamolo and Mathew Morgan
Sensors 2022, 22(18), 6882; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186882 - 12 Sep 2022
Cited by 3 | Viewed by 2043
Abstract
Monitoring and gathering data on sporting activities holds significant promise for athletes, equipment developers, and physical fitness clinicians. Wireless Body Area Networks are being used in sporting environments as a means of gathering data, providing feedback, and helping to gain understanding of athletic [...] Read more.
Monitoring and gathering data on sporting activities holds significant promise for athletes, equipment developers, and physical fitness clinicians. Wireless Body Area Networks are being used in sporting environments as a means of gathering data, providing feedback, and helping to gain understanding of athletic activities. Applying WBANs to skiing situations, which have higher vibration, velocities, and damp environments than many other sports, can open up opportunities to understand the dynamics of skiing equipment behaviors, skiing routes on mountains, and how individuals react when skiing. To support these outcomes, a prototype WBAN-style off the shelf component system called SkiMon was proposed, implemented, and tested. The SkiMon system uses inexpensive ESP8266, Raspberry Pi, and sensor devices to gather high quality motion and location tracking data on skiers in real-world skiing conditions. By using IEEE 802.11b/g/n wireless networks, SkiMon is able to sample data at a minimum of 50 Hz, which is enough to model most ski vibration behaviors. These data results are shown to reflect ground truth 3D maps and the acceleration data comports with earlier works on ski vibration testing. Overall, a WBAN-based commodity components solution shows promise as a high quality sensor platform for tracking and modeling skiing activities. Full article
(This article belongs to the Special Issue Wireless Body Area Networks (WBAN))
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17 pages, 12186 KiB  
Article
Toward Dependable Internet of Medical Things: IEEE 802.15.6 Ultra-Wideband Physical Layer Utilizing Superorthogonal Convolutional Code
by Kento Takabayashi, Hirokazu Tanaka and Katsumi Sakakibara
Sensors 2022, 22(6), 2172; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062172 - 10 Mar 2022
Cited by 2 | Viewed by 1887
Abstract
Wireless body area networks (WBANs) are attracting attention as a very important technology for realizing an Internet of Medical Things (IoMT). IEEE 802.15.6 is well known as one of the international standards for WBANs for the IoMT. This article proposes the combination of [...] Read more.
Wireless body area networks (WBANs) are attracting attention as a very important technology for realizing an Internet of Medical Things (IoMT). IEEE 802.15.6 is well known as one of the international standards for WBANs for the IoMT. This article proposes the combination of the IEEE 802.15.6 ultra-wideband (UWB) physical layer (PHY) with a super orthogonal convolutional code (SOOC) and evaluates its performance as a dependable WBAN. Numerical results show that sufficient dependability cannot be obtained with the error-correcting code specified in IEEE 802.15.6 when applying the single pulse option, while both high energy efficiency and dependability can be obtained by applying an SOCC. In addition, it is confirmed that higher dependability can be obtained by combining an SOCC with a Reed–Solomon (RS) code with a coding rate that is almost the same as the error correction code specified in the standard. Furthermore, the results indicate that high dependability and energy efficiency can be obtained by adjusting the SOCC coding rate and UWB PHY parameters, even in the burst pulse option. The SOCC-applied UWB PHY of this research satisfies the high requirements of the IoMT. Full article
(This article belongs to the Special Issue Wireless Body Area Networks (WBAN))
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Review

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32 pages, 919 KiB  
Review
Access Control, Key Management, and Trust for Emerging Wireless Body Area Networks
by Ahmad Salehi Shahraki, Hagen Lauer, Marthie Grobler, Amin Sakzad and Carsten Rudolph
Sensors 2023, 23(24), 9856; https://0-doi-org.brum.beds.ac.uk/10.3390/s23249856 - 15 Dec 2023
Viewed by 913
Abstract
Wireless Body Area Networks (WBANs) are an emerging industrial technology for monitoring physiological data. These networks employ medical wearable and implanted biomedical sensors aimed at improving quality of life by providing body-oriented services through a variety of industrial sensing gadgets. The sensors collect [...] Read more.
Wireless Body Area Networks (WBANs) are an emerging industrial technology for monitoring physiological data. These networks employ medical wearable and implanted biomedical sensors aimed at improving quality of life by providing body-oriented services through a variety of industrial sensing gadgets. The sensors collect vital data from the body and forward this information to other nodes for further services using short-range wireless communication technology. In this paper, we provide a multi-aspect review of recent advancements made in this field pertaining to cross-domain security, privacy, and trust issues. The aim is to present an overall review of WBAN research and projects based on applications, devices, and communication architecture. We examine current issues and challenges with WBAN communications and technologies, with the aim of providing insights for a future vision of remote healthcare systems. We specifically address the potential and shortcomings of various Wireless Body Area Network (WBAN) architectures and communication schemes that are proposed to maintain security, privacy, and trust within digital healthcare systems. Although current solutions and schemes aim to provide some level of security, several serious challenges remain that need to be understood and addressed. Our aim is to suggest future research directions for establishing best practices in protecting healthcare data. This includes monitoring, access control, key management, and trust management. The distinguishing feature of this survey is the combination of our review with a critical perspective on the future of WBANs. Full article
(This article belongs to the Special Issue Wireless Body Area Networks (WBAN))
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