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Sensors
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Body Sensor Networks and Wearables for Health Monitoring
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Body Sensor Networks and Wearables for Health Monitoring

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3225

Special Issue Editor

Dr. Dario Salvi

E-Mail Website
Guest Editor
Department of Computer Science and Media Technology, Malmo University, 211 19 Malmo, Sweden
Interests: eHealth; mobile-health; digital-health; telehealth; IoT; IoMT; medical devices; medical informatics; AI; signal processing; sensor networks; wearable devices; embedded systems; user aspects; usability and acceptance; privacy; trust
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, wearable sensors are used in a number of applications, from fashion to fitness and security; however, the most promising application of such technology is within healthcare. When combined with connectivity (thus becoming "Internet of Things" devices), wearable sensors can be exploited for the real-time monitoring of physiology (e.g., heart rate, respiration, sleep) and activities (e.g., steps, movement, activity detection). These data can be used to support healthcare, improve or automatize diagnosis and treatments, and foster healthy and independent living. Along with benefits, important challenges come in terms of interoperability, reliability, power consumption, usability, ergonomicity, data quality, data protection, security, and reliability. This special issue aims to explore these challenges and their potential solutions.

The main topics of this Special Issue include, but are not limited to, the following:

  • Low power wireless communication for wearable sensors;
  • Body-centric wireless networks;
  • Interoperability and standards for wearable health monitoring;
  • Security models and technologies for wearable health monitoring;
  • Applications of networked wearable sensors for health monitoring;
  • Algorithms for extracting meaningful information from wearable sensors;
  • Data collection and processing architectures;
  • Usability, ergonomicity, and biocompatibility of wearable devices and their applications;
  • Users' perceptions about the privacy and reliability of wearable health monitoring;
  • Clinical trials and pilot studies involving body sensor networks and wearable health monitoring.

Dr. Dario Salvi
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. 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 sensor networks
  • wearable sensors
  • health monitoring
  • Internet of Medical Things
  • mobile-health
  • digital-health

Published Papers (3 papers)

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Research

24 pages, 3730 KiB  
Article
The Development of a New Vagus Nerve Simulation Electroceutical to Improve the Signal Attenuation in a Living Implant Environment
by Daeil Jo, Hyunung Lee, Youlim Jang, Paul Oh and Yongjin Kwon
Sensors 2024, 24(10), 3172; https://0-doi-org.brum.beds.ac.uk/10.3390/s24103172 - 16 May 2024
Viewed by 210
Abstract
An electroceutical is a medical device that uses electrical signals to control biological functions. It can be inserted into the human body as an implant and has several crucial advantages over conventional medicines for certain diseases. This research develops a new vagus nerve [...] Read more.
An electroceutical is a medical device that uses electrical signals to control biological functions. It can be inserted into the human body as an implant and has several crucial advantages over conventional medicines for certain diseases. This research develops a new vagus nerve simulation (VNS) electroceutical through an innovative approach to overcome the communication limitations of existing devices. A phased array antenna with a better communication performance was developed and applied to the electroceutical prototype. In order to effectively respond to changes in communication signals, we developed the steering algorithm and firmware, and designed the smart communication protocol that operates at a low power that is safe for the patients. This protocol is intended to improve a communication sensitivity related to the transmission and reception distance. Based on this technical approach, the heightened effectiveness and safety of the prototype have been ascertained, with the actual clinical tests using live animals. We confirmed the signal attenuation performance to be excellent, and a smooth communication was achieved even at a distance of 7 m. The prototype showed a much wider communication range than any other existing products. Through this, it is conceivable that various problems due to space constraints can be resolved, hence presenting many benefits to the patients whose last resort to the disease is the VNS electroceutical. Full article
(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)
24 pages, 1134 KiB  
Article
Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT
by Sara Caramaschi, Carl Magnus Olsson, Elizabeth Orchard, Jackson Molloy and Dario Salvi
Sensors 2024, 24(8), 2632; https://0-doi-org.brum.beds.ac.uk/10.3390/s24082632 - 20 Apr 2024
Viewed by 438
Abstract
As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised [...] Read more.
As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users’ previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions. Full article
(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)
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20 pages, 2753 KiB  
Article
A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment
by Sylvain Jung, Nicolas de l’Escalopier, Laurent Oudre, Charles Truong, Eric Dorveaux, Louis Gorintin and Damien Ricard
Sensors 2023, 23(8), 4000; https://0-doi-org.brum.beds.ac.uk/10.3390/s23084000 - 14 Apr 2023
Viewed by 1761
Abstract
This paper presents a novel approach to creating a graphical summary of a subject’s activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. [...] Read more.
This paper presents a novel approach to creating a graphical summary of a subject’s activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time series collected while monitoring patients in Semi Free-Living Environments are often long and complex, our contribution relies on an innovative pipeline of signal processing methods and machine learning algorithms. Once learned, the graphical representation is able to sum up all activities present in the data and can quickly be applied to newly acquired time series. In a nutshell, raw data from inertial measurement units are first segmented into homogeneous regimes with an adaptive change-point detection procedure, then each segment is automatically labeled. Then, features are extracted from each regime, and lastly, a score is computed using these features. The final visual summary is constructed from the scores of the activities and their comparisons to healthy models. This graphical output is a detailed, adaptive, and structured visualization that helps better understand the salient events in a complex gait protocol. Full article
(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)
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