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Wearable Communication and Sensing Systems: Advances and Challenges
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Wearable Communication and Sensing Systems: Advances and Challenges

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

Deadline for manuscript submissions: closed (25 June 2024) | Viewed by 30647

Special Issue Editors

Dr. Wenda Li

E-Mail Website
Guest Editor
Department of Security and Crime Science, University College London, London WC1E 6BT, UK
Interests: passive radar; activity recognition; remote sensing; ISAC; digital health
Special Issues, Collections and Topics in MDPI journals
Dr. Yue Tian

E-Mail Website
Guest Editor
Fujian Key Laboratory of Communication Network and Information Processing, School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
Interests: integrated sensing; computation and communication; wireless sensor networks; mobile edge computing
Special Issues, Collections and Topics in MDPI journals
Dr. Shelly Vishwakarma

E-Mail Website
Guest Editor
Department of Security and Crime Science, University College London, London WC1E 6BT, UK
Interests: active radar; passive radar; through-the-wall sensing; activity recognition; remote sensing; signal processing

Special Issue Information

Dear Colleagues,

Wearable devices are the next big thing in wireless applications for communication, sensing and health monitoring. History will show that the FitBit and the Apple Watch are only early primitive devices, and the next wave of smart wearables will revolutionize the way we live. Therefore, wearable communication systems, antennas and sensing methods are crucial for the development of next-generation technologies in wearable medical devices, telemedicine, vital sign detection and patient monitoring.

The goal of this Special Issue is to bring together scientists, researchers, practitioners and service providers in order to publish high-quality manuscripts related to sensing technologies, data analytics and their application in predictive and personalized healthcare. Potential topics include but are not limited to:

  • Wearable health monitoring systems;
  • Wearable sensor networks;
  • Wearable wireless communication;
  • Antenna design for wearables;
  • Wearable device trends;
  • Wearable device applications;
  • Wearable device for underwater sensing and control;
  • Wearable device signal sensing and processing;
  • Multi-source physiological signal sensing.

Dr. Wenda Li
Dr. Yue Tian
Dr. Shelly Vishwakarma
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.

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Published Papers (7 papers)

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Research

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13 pages, 1978 KiB  
Article
Unobtrusive Sensors for Synchronous Monitoring of Different Breathing Parameters in Care Environments
by Imran Saied, Aaesha Alzaabi and Tughrul Arslan
Sensors 2024, 24(7), 2233; https://doi.org/10.3390/s24072233 - 31 Mar 2024
Viewed by 970
Abstract
Respiratory problems are common amongst older people. The rapid increase in the ageing population has led to a need for developing technologies that can monitor such conditions unobtrusively. This paper presents a novel study that investigates Wi-Fi and ultra-wideband (UWB) antenna sensors to [...] Read more.
Respiratory problems are common amongst older people. The rapid increase in the ageing population has led to a need for developing technologies that can monitor such conditions unobtrusively. This paper presents a novel study that investigates Wi-Fi and ultra-wideband (UWB) antenna sensors to simultaneously monitor two different breathing parameters: respiratory rate, and exhaled breath. Experiments were carried out with two subjects undergoing three breathing cases in breaths per minute (BPM): (1) slow breathing (12 BPM), (2) moderate breathing (20 BPM), and (3) fast breathing (28 BPM). Respiratory rates were captured by Wi-Fi sensors, and the data were processed to extract the respiration rates and compared with a metronome that controlled the subjects’ breathing. On the other hand, exhaled breath data were captured by a UWB antenna using a vector network analyser (VNA). Corresponding reflection coefficient data (S11) were obtained from the subjects at the time of exhalation and compared with S11 in free space. The exhaled breath data from the UWB antenna were compared with relative humidity, which was measured with a digital psychrometer during the breathing exercises to determine whether a correlation existed between the exhaled breath’s water vapour content and recorded S11 data. Finally, captured respiratory rate and exhaled breath data from the antenna sensors were compared to determine whether a correlation existed between the two parameters. The results showed that the antenna sensors were capable of capturing both parameters simultaneously. However, it was found that the two parameters were uncorrelated and independent of one another. Full article
(This article belongs to the Special Issue Wearable Communication and Sensing Systems: Advances and Challenges)
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27 pages, 1121 KiB  
Article
ICT Framework for Supporting Applied Behavior Analysis in the Social Inclusion of Children with Neurodevelopmental Disorders
by Sara Jayousi, Alessio Martinelli, Paolo Lucattini and Lorenzo Mucchi
Sensors 2023, 23(15), 6914; https://doi.org/10.3390/s23156914 - 3 Aug 2023
Cited by 3 | Viewed by 2325
Abstract
The applied behavior analysis (ABA) model emphasizes observable and measurable behaviors by carrying out decision making using experimental data (behavioral observation assessment strategies). In this framework, information and communication technology (ICT) becomes highly suitable for enhancing the efficiency and effectiveness of the methodology. [...] Read more.
The applied behavior analysis (ABA) model emphasizes observable and measurable behaviors by carrying out decision making using experimental data (behavioral observation assessment strategies). In this framework, information and communication technology (ICT) becomes highly suitable for enhancing the efficiency and effectiveness of the methodology. This paper aims to delve into the potential of ICT in providing innovative solutions to support ABA applications. It focuses on how ICT can contribute to fostering social inclusion with respect to children with neurodevelopmental disorders. ICT offers advanced solutions for continuous and context-aware monitoring, as well as automatic real-time behavior assessments. Wireless sensor systems (wearable perceptual, biomedical, motion, location, and environmental sensors) facilitate real-time behavioral monitoring in various contexts, enabling the collection of behavior-related data that may not be readily evident in traditional observational studies. Moreover, the incorporation of artificial intelligence algorithms that are appropriately trained can further assist therapists throughout the different phases of ABA therapy. These algorithms can provide intervention guidelines and deliver an automatic behavioral analysis that is personalized to the child’s unique profile. By leveraging the power of ICT, ABA practitioners can benefit from cutting-edge technological advancements to optimize their therapeutic interventions and outcomes for children with neurodevelopmental disorders, ultimately contributing to their social inclusion and overall wellbeing. Full article
(This article belongs to the Special Issue Wearable Communication and Sensing Systems: Advances and Challenges)
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22 pages, 2551 KiB  
Article
The Retrieval and Effect of Core Parameters for Near-Field Inter-Body Coupling Communication
by Xu Zhang, Yong Song, Ya Zhou, Maoyuan Li, Wu Ren, Yizhu Ma, Changxiang Li and Yubo Cao
Sensors 2023, 23(12), 5521; https://doi.org/10.3390/s23125521 - 12 Jun 2023
Viewed by 1354
Abstract
The potential of the Internet of Body (IoB) to support healthcare systems in the future lies in its ability to enable proactive wellness screening through the early detection and prevention of diseases. One promising technology for facilitating IoB applications is near-field inter-body coupling [...] Read more.
The potential of the Internet of Body (IoB) to support healthcare systems in the future lies in its ability to enable proactive wellness screening through the early detection and prevention of diseases. One promising technology for facilitating IoB applications is near-field inter-body coupling communication (NF-IBCC), which features lower power consumption and higher data security when compared to conventional radio frequency (RF) communication. However, designing efficient transceivers requires a profound understanding of the channel characteristics of NF-IBCC, which remain unclear due to significant differences in the magnitude and passband characteristics of existing research. In response to this problem, this paper clarifies the physical mechanisms of the differences in the magnitude and passband characteristics of NF-IBCC channel characteristics in existing research work through the core parameters that determine the gain of the NF-IBCC system. The core parameters of NF-IBCC are extracted through the combination of transfer functions, finite element simulations, and physical experiments. The core parameters include the inter-body coupling capacitance (CH), the load impedance (ZL), and the capacitance (Cair), coupled by two floating transceiver grounds. The results illustrate that CH, and particularly Cair, primarily determine the gain magnitude. Moreover, ZL mainly determines the passband characteristics of the NF-IBCC system gain. Based on these findings, we propose a simplified equivalent circuit model containing only core parameters, which can accurately capture the gain characteristics of the NF-IBCC system and help to concisely describe the channel characteristics of the system. This work lays a theoretical foundation for developing efficient and reliable NF-IBCC systems that can support IoB for early disease detection and prevention in healthcare applications. The potential benefits of IoB and NF-IBCC technology can, thus, be fully realized by developing optimized transceiver designs based on a comprehensive understanding of the channel characteristics. Full article
(This article belongs to the Special Issue Wearable Communication and Sensing Systems: Advances and Challenges)
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13 pages, 1030 KiB  
Article
Improving BLE-Based Passive Human Sensing with Deep Learning
by Giancarlo Iannizzotto, Lucia Lo Bello and Andrea Nucita
Sensors 2023, 23(5), 2581; https://doi.org/10.3390/s23052581 - 26 Feb 2023
Cited by 2 | Viewed by 2437
Abstract
Passive Human Sensing (PHS) is an approach to collecting data on human presence, motion or activities that does not require the sensed human to carry devices or participate actively in the sensing process. In the literature, PHS is generally performed by exploiting the [...] Read more.
Passive Human Sensing (PHS) is an approach to collecting data on human presence, motion or activities that does not require the sensed human to carry devices or participate actively in the sensing process. In the literature, PHS is generally performed by exploiting the Channel State Information variations of dedicated WiFi, affected by human bodies obstructing the WiFi signal propagation path. However, the adoption of WiFi for PHS has some drawbacks, related to power consumption, large-scale deployment costs and interference with other networks in nearby areas. Bluetooth technology and, in particular, its low-energy version Bluetooth Low Energy (BLE), represents a valid candidate solution to the drawbacks of WiFi, thanks to its Adaptive Frequency Hopping (AFH) mechanism. This work proposes the application of a Deep Convolutional Neural Network (DNN) to improve the analysis and classification of the BLE signal deformations for PHS using commercial standard BLE devices. The proposed approach was applied to reliably detect the presence of human occupants in a large and articulated room with only a few transmitters and receivers and in conditions where the occupants do not directly occlude the Line of Sight between transmitters and receivers. This paper shows that the proposed approach significantly outperforms the most accurate technique found in the literature when applied to the same experimental data. Full article
(This article belongs to the Special Issue Wearable Communication and Sensing Systems: Advances and Challenges)
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19 pages, 5644 KiB  
Article
Real-Time Step Length Estimation in Indoor and Outdoor Scenarios
by Zanru Yang, Le Chung Tran, Farzad Safaei, Anh Tuyen Le and Attaphongse Taparugssanagorn
Sensors 2022, 22(21), 8472; https://doi.org/10.3390/s22218472 - 3 Nov 2022
Cited by 3 | Viewed by 2112
Abstract
In this paper, human step length is estimated based on the wireless channel properties and the received signal strength indicator (RSSI) method. The path loss between two ankles, called the on-ankle path loss, is converted from the RSSI, which is measured by our [...] Read more.
In this paper, human step length is estimated based on the wireless channel properties and the received signal strength indicator (RSSI) method. The path loss between two ankles, called the on-ankle path loss, is converted from the RSSI, which is measured by our developed wearable hardware in indoor and outdoor ambulation scenarios. The human walking step length is estimated by a reliable range of RSSI values. The upper threshold and the lower threshold of this range are determined experimentally. This paper advances our previous step length measurement technique by proposing a novel exponential weighted moving average (EWMA) algorithm to update the upper and lower thresholds, and thus the step length estimation, recursively. The EWMA algorithm allows our measurement technique to process each shorter subset of the dataset, called a time window, and estimate the step length, rather than having to process the whole dataset at a time. The step length is periodically updated on the fly when the time window is “sliding” forwards. Thus, the EWMA algorithm facilitates the step length estimation in real-time. The impact of the EWMA parameter is analysed, and the optimal parameter is discovered for different experimental scenarios. Our experiments show that the EWMA algorithm could achieve comparable accuracy as our previously proposed technique with errors as small as 3.02% and 0.30% for the indoor and outdoor scenarios, respectively, while the processing time required to output an estimation of the step length could be significantly shortened by 53.96% and 60% for the indoor walking and outdoor walking, respectively. Full article
(This article belongs to the Special Issue Wearable Communication and Sensing Systems: Advances and Challenges)
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Review

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21 pages, 2765 KiB  
Review
5G Technology in Healthcare and Wearable Devices: A Review
by Delshi Howsalya Devi, Kumutha Duraisamy, Ammar Armghan, Meshari Alsharari, Khaled Aliqab, Vishal Sorathiya, Sudipta Das and Nasr Rashid
Sensors 2023, 23(5), 2519; https://doi.org/10.3390/s23052519 - 24 Feb 2023
Cited by 53 | Viewed by 16285
Abstract
Wearable devices with 5G technology are currently more ingrained in our daily lives, and they will now be a part of our bodies too. The requirement for personal health monitoring and preventive disease is increasing due to the predictable dramatic increase in the [...] Read more.
Wearable devices with 5G technology are currently more ingrained in our daily lives, and they will now be a part of our bodies too. The requirement for personal health monitoring and preventive disease is increasing due to the predictable dramatic increase in the number of aging people. Technologies with 5G in wearables and healthcare can intensely reduce the cost of diagnosing and preventing diseases and saving patient lives. This paper reviewed the benefits of 5G technologies, which are implemented in healthcare and wearable devices such as patient health monitoring using 5G, continuous monitoring of chronic diseases using 5G, management of preventing infectious diseases using 5G, robotic surgery using 5G, and 5G with future of wearables. It has the potential to have a direct effect on clinical decision making. This technology could improve patient rehabilitation outside of hospitals and monitor human physical activity continuously. This paper draws the conclusion that the widespread adoption of 5G technology by healthcare systems enables sick people to access specialists who would be unavailable and receive correct care more conveniently. Full article
(This article belongs to the Special Issue Wearable Communication and Sensing Systems: Advances and Challenges)
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28 pages, 1713 KiB  
Review
A Systematic Literature Review of Handheld Augmented Reality Solutions for People with Disabilities
by Matea Žilak, Željka Car and Ivana Čuljak
Sensors 2022, 22(20), 7719; https://doi.org/10.3390/s22207719 - 11 Oct 2022
Cited by 12 | Viewed by 3882
Abstract
Mobile applications on smartphones and tablets have become part of our everyday lives. The number of augmented reality (AR) technology applications is also increasing. Augmented reality has proven to be effective in various areas of human life, from education, marketing, and training to [...] Read more.
Mobile applications on smartphones and tablets have become part of our everyday lives. The number of augmented reality (AR) technology applications is also increasing. Augmented reality has proven to be effective in various areas of human life, from education, marketing, and training to navigation. All people have the right to access information and use available technologies, but not everyone has the same opportunities. To contribute to the digital inclusion of people who are often disadvantaged in this regard, we should think about the accessibility of digital technologies, including mobile augmented reality (MAR). The specificity of MAR is a new way of human–computer interaction compared to traditional mobile solutions. The objective of this review paper is to analyze the handheld AR solutions developed for people with different disabilities to identify accessibility challenges related to interaction when performing different tasks in AR. It also explores and presents accessibility features and other accessibility best practices, as well as potential future research directions related to the personalization and customization of such solutions for individuals. The results of this literature review can contribute to the creation of accessibility guidelines in the field of handheld AR and encourage the development of accessible AR solutions that can benefit not only people with disabilities but also people without disabilities. Full article
(This article belongs to the Special Issue Wearable Communication and Sensing Systems: Advances and Challenges)
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