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Special Issue "Medical Sensors and Body Area Networks"

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A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Actuators, Sensors and Devices".

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 2822

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Prof. Dr. Yu-Dong Zhang

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Informatics Building, School of Informatics, University of Leicester, University Road, Leicester LE1 7RH, UK
Interests: artificial intelligence; medical sensor; image processing; deep learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Juan Manuel Gorriz

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Department of Signal Theory, Telematics and Communications, University of Granada, 18071 Granada, Spain
Interests: neuroimaging; statistical learning theory; biomedical signal processing; computer-aided diagnosis
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Dr. Shuihua Wang

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Department of Mathematics, University of Leicester, University Road, Leicester LE1 7RH, UK
Interests: deep learning; unsupervised learning
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Special Issue Information

Dear Colleagues,

Traditional medical sensors can measure pressure, airflow, force, oxygen, pulse, temperature, etc. Recently, the medical imaging sensors, such as CT, MRI, PET, and SPECT, have played an important role in checking the anatomy and physiological processes of the body. Meanwhile, body area network technologies utilize low-power wireless devices either carried or embedded inside or on the body. This Special Issue plans to report the recent advances in medical sensors and body area networks.

Prof. Dr. Yu-Dong Zhang
Prof. Dr. Juan Manuel Gorriz
Dr. Shuihua Wang
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. Journal of Sensor and Actuator Networks 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 1600 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

medical sensor
imaging sensor
computed tomography
magnetic resonance imaging
body area network
security and privacy
health and wellness monitoring
personalized medicine

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Open AccessEditorial

Modern Forms and New Challenges in Medical Sensors and Body Area Networks

Yudong Zhang

Juan Manuel Gorriz

and

Shuihua Wang

J. Sens. Actuator Netw. 2022, 11(4), 79; https://doi.org/10.3390/jsan11040079 - 23 Nov 2022

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Abstract

Traditional medical sensors/monitors can measure pressure, airflow, force, oxygen, pulse, temperature, etc [...] Full article

(This article belongs to the Special Issue Medical Sensors and Body Area Networks)

Research

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Open AccessArticle

An Online Method for Supporting and Monitoring Repetitive Physical Activities Based on Restricted Boltzmann Machines

and

J. Sens. Actuator Netw. 2023, 12(5), 70; https://doi.org/10.3390/jsan12050070 - 22 Sep 2023

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Abstract

Human activity recognition has been widely used to monitor users during physical activities. By embedding a pre-trained model into wearable devices with an inertial measurement unit, it is possible to identify the activity being executed, count steps and activity duration time, and even predict when the user should hydrate himself. Despite these interesting applications, these approaches are limited by a set of pre-trained activities, making them unable to learn new human activities. In this paper, we introduce a novel approach for generating runtime models to give the users feedback that helps them to correctly perform repetitive physical activities. To perform a distributed analysis, the methodology focuses on applying the proposed method to each specific body segment. The method adopts the Restricted Boltzmann Machine to learn the patterns of repetitive physical activities and, at the same time, provides suggestions for adjustments if the repetition is not consistent with the model. The learning and the suggestions are both based on inertial measurement data mainly considering movement acceleration and amplitude. The results show that by applying the model’s suggestions to the evaluation data, the adjusted output was up to 3.68x more similar to the expected movement than the original data. Full article

(This article belongs to the Special Issue Medical Sensors and Body Area Networks)

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Open AccessArticle

Evaluating Edge Computing and Compression for Remote Cuff-Less Blood Pressure Monitoring

and

J. Sens. Actuator Netw. 2023, 12(1), 2; https://doi.org/10.3390/jsan12010002 - 26 Dec 2022

Cited by 1 | Viewed by 1187

Abstract

Remote health monitoring systems play an important role in the healthcare sector. Edge computing is a key enabler for realizing these systems, where it is required to collect big data while providing real-time guarantees. In this study, we focus on remote cuff-less blood pressure (BP) monitoring through electrocardiogram (ECG) as a case study to evaluate the benefits of edge computing and compression. First, we investigate the state-of-the-art algorithms for BP estimation and ECG compression. Second, we develop a system to measure the ECG, estimate the BP, and store the results in the cloud with three different configurations: (i) estimation in the edge, (ii) estimation in the cloud, and (iii) estimation in the cloud with compressed transmission. Third, we evaluate the three approaches in terms of application latency, transmitted data volume, and power usage. In experiments with batches of 64 ECG samples, the edge computing approach has reduced average application latency by 15%, average power usage by 19%, and total transmitted volume by 85%, confirming that edge computing improves system performance significantly. Compressed transmission proved to be an alternative when network bandwidth is limited and edge computing is impractical. Full article

(This article belongs to the Special Issue Medical Sensors and Body Area Networks)

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