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Embedded Sensor Systems for Health

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

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 32389

Special Issue Editors

Division of Intelligent Future Technologies, Mälardalen University, 721 23 Västerås, Sweden
Interests: physiological measurements; measurement methods; sensor systems
Division of Intelligent Future Technologies, Mälardalen University, 721 23 Västerås, Sweden
Interests: health technology; biomedical engineering; signal processing; wearable body sensors; e-health and m-health; biomedical sensor systems; non-invasive sensor systems; motion analysis; fall detection; fall prevention; blood flow measurements; end-user compliance; user acceptance
Special Issues, Collections and Topics in MDPI journals
Division of Networked and Embedded Systems, Mälardalen University, 721 23 Västerås, Sweden
Interests: computer network performance; predictability issues; communication for embedded systems (e.g., Cyber-Physical Systems (CPS) or Internet of Things (IoT)); methods and methodology for delay prediction; reliability issues (safety, security) for wireless networks; industrial automation and health applications
Division of Intelligent Future Technologies, Mälardalen University, 721 23 Västerås, Sweden
Interests: computational intelligence techniques; machine learning; big data analytics; evolutionary computing; fuzzy systems; uncertainty management; multi-sensor data fusion; building self-learning; adaptive systems in industrial and medical domains

Special Issue Information

Dear Colleagues,

The demographic changes, with an increasing elderly population and with an increased incidence of multiple chronic diseases, accentuate the need for more advanced sensor systems to support and monitor health conditions. New tools are needed in order to maintain the current level of care also in the future. Intelligent sensor systems including smart sensors, reliable data acquisition, safe and secure data communication and predictive data analytics, enable health monitoring anytime, anywhere and provides intelligent decision support with regard to early detection, diagnosis, prognosis, and health promotion. This Special Issue solicits papers that address various aspects of intelligent sensor systems for health monitoring, that contribute to more reliable health assessment and promotion, in a timely manner.

Dr. Mia Folke
Prof. Dr. Maria Linden
Prof. Mats Björkman
Prof. Dr. Ning Xiong
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

  • intelligent sensor systems
  • health monitoring
  • data acquisition
  • physiological data
  • time series data
  • machine learning
  • multi-sensor data fusion
  • reliable data communication

Published Papers (6 papers)

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Research

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30 pages, 5632 KiB  
Article
Medical Devices with Embedded Sensor Systems: Design and Development Methodology for Start-Ups
by Nerea Arandia, Jose Ignacio Garate and Jon Mabe
Sensors 2023, 23(5), 2578; https://0-doi-org.brum.beds.ac.uk/10.3390/s23052578 - 26 Feb 2023
Cited by 1 | Viewed by 2681
Abstract
Embedded systems have become a key technology for the evolution of medical devices. However, the regulatory requirements that must be met make designing and developing these devices challenging. As a result, many start-ups attempting to develop medical devices fail. Therefore, this article presents [...] Read more.
Embedded systems have become a key technology for the evolution of medical devices. However, the regulatory requirements that must be met make designing and developing these devices challenging. As a result, many start-ups attempting to develop medical devices fail. Therefore, this article presents a methodology to design and develop embedded medical devices while minimising the economic investment during the technical risk stages and encouraging customer feedback. The proposed methodology is based on the execution of three stages: Development Feasibility, Incremental and Iterative Prototyping, and Medical Product Consolidation. All this is completed in compliance with the applicable regulations. The methodology mentioned above is validated through practical use cases in which the development of a wearable device for monitoring vital signs is the most relevant. The presented use cases sustain the proposed methodology, for the devices were successfully CE marked. Moreover, ISO 13485 certification is obtained by following the proposed procedures. Full article
(This article belongs to the Special Issue Embedded Sensor Systems for Health)
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28 pages, 931 KiB  
Article
Embedded Sensor Systems in Medical Devices: Requisites and Challenges Ahead
by Nerea Arandia, Jose Ignacio Garate and Jon Mabe
Sensors 2022, 22(24), 9917; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249917 - 16 Dec 2022
Cited by 6 | Viewed by 10280
Abstract
The evolution of technology enables the design of smarter medical devices. Embedded Sensor Systems play an important role, both in monitoring and diagnostic devices for healthcare. The design and development of Embedded Sensor Systems for medical devices are subjected to standards and regulations [...] Read more.
The evolution of technology enables the design of smarter medical devices. Embedded Sensor Systems play an important role, both in monitoring and diagnostic devices for healthcare. The design and development of Embedded Sensor Systems for medical devices are subjected to standards and regulations that will depend on the intended use of the device as well as the used technology. This article summarizes the challenges to be faced when designing Embedded Sensor Systems for the medical sector. With this aim, it presents the innovation context of the sector, the stages of new medical device development, the technological components that make up an Embedded Sensor System and the regulatory framework that applies to it. Finally, this article highlights the need to define new medical product design and development methodologies that help companies to successfully introduce new technologies in medical devices. Full article
(This article belongs to the Special Issue Embedded Sensor Systems for Health)
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15 pages, 3649 KiB  
Article
Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images
by Ranpreet Kaur, Hamid GholamHosseini, Roopak Sinha and Maria Lindén
Sensors 2022, 22(3), 1134; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031134 - 02 Feb 2022
Cited by 69 | Viewed by 4296
Abstract
Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to [...] Read more.
Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC2017, and ISIC 2020). We evaluated the model based on accuracy, precision, recall, specificity, and F1-score. The proposed DCNN classifier achieved accuracies of 81.41%, 88.23%, and 90.42% on the ISIC 2016, 2017, and 2020 datasets, respectively, demonstrating high performance compared with the other state-of-the-art networks. Therefore, this proposed approach could provide a less complex and advanced framework for automating the melanoma diagnostic process and expediting the identification process to save a life. Full article
(This article belongs to the Special Issue Embedded Sensor Systems for Health)
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Review

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22 pages, 1153 KiB  
Review
New Hemodynamic Parameters in Peri-Operative and Critical Care—Challenges in Translation
by Laura Bogatu, Simona Turco, Massimo Mischi, Lars Schmitt, Pierre Woerlee, Rick Bezemer, Arthur R. Bouwman, Erik H. H. M. Korsten and Jens Muehlsteff
Sensors 2023, 23(4), 2226; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042226 - 16 Feb 2023
Cited by 2 | Viewed by 3998
Abstract
Hemodynamic monitoring technologies are evolving continuously—a large number of bedside monitoring options are becoming available in the clinic. Methods such as echocardiography, electrical bioimpedance, and calibrated/uncalibrated analysis of pulse contours are becoming increasingly common. This is leading to a decline in the use [...] Read more.
Hemodynamic monitoring technologies are evolving continuously—a large number of bedside monitoring options are becoming available in the clinic. Methods such as echocardiography, electrical bioimpedance, and calibrated/uncalibrated analysis of pulse contours are becoming increasingly common. This is leading to a decline in the use of highly invasive monitoring and allowing for safer, more accurate, and continuous measurements. The new devices mainly aim to monitor the well-known hemodynamic variables (e.g., novel pulse contour, bioreactance methods are aimed at measuring widely-used variables such as blood pressure, cardiac output). Even though hemodynamic monitoring is now safer and more accurate, a number of issues remain due to the limited amount of information available for diagnosis and treatment. Extensive work is being carried out in order to allow for more hemodynamic parameters to be measured in the clinic. In this review, we identify and discuss the main sensing strategies aimed at obtaining a more complete picture of the hemodynamic status of a patient, namely: (i) measurement of the circulatory system response to a defined stimulus; (ii) measurement of the microcirculation; (iii) technologies for assessing dynamic vascular mechanisms; and (iv) machine learning methods. By analyzing these four main research strategies, we aim to convey the key aspects, challenges, and clinical value of measuring novel hemodynamic parameters in critical care. Full article
(This article belongs to the Special Issue Embedded Sensor Systems for Health)
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25 pages, 872 KiB  
Review
Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review
by Mohammad Moshawrab, Mehdi Adda, Abdenour Bouzouane, Hussein Ibrahim and Ali Raad
Sensors 2022, 22(19), 7472; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197472 - 02 Oct 2022
Cited by 10 | Viewed by 3332
Abstract
Today’s world is changing dramatically due to the influence of various factors. Whether due to the rapid development of technological tools, advances in telecommunication methods, global economic and social events, or other reasons, almost everything is changing. As a result, the concepts of [...] Read more.
Today’s world is changing dramatically due to the influence of various factors. Whether due to the rapid development of technological tools, advances in telecommunication methods, global economic and social events, or other reasons, almost everything is changing. As a result, the concepts of a “job” or work have changed as well, with new work shifts being introduced and the office no longer being the only place where work is done. In addition, our non-stop active society has increased the stress and pressure at work, causing fatigue to spread worldwide and becoming a global problem. Moreover, it is medically proven that persistent fatigue is a cause of serious diseases and health problems. Therefore, monitoring and detecting fatigue in the workplace is essential to improve worker safety in the long term. In this paper, we provide an overview of the use of smart wearable devices to monitor and detect occupational physical fatigue. In addition, we present and discuss the challenges that hinder this field and highlight what can be done to advance the use of smart wearables in workplace fatigue detection. Full article
(This article belongs to the Special Issue Embedded Sensor Systems for Health)
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39 pages, 710 KiB  
Review
A Systematic Review of Wearable Sensors for Monitoring Physical Activity
by Annica Kristoffersson and Maria Lindén
Sensors 2022, 22(2), 573; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020573 - 12 Jan 2022
Cited by 26 | Viewed by 6428
Abstract
This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological [...] Read more.
This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing. Full article
(This article belongs to the Special Issue Embedded Sensor Systems for Health)
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