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Smart Sensors for Medical Data Acquisition and Analysis
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Smart Sensors for Medical Data Acquisition and Analysis

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 10602

Special Issue Editor

Dr. Manuel Dominguez-Morales

E-Mail Website
Guest Editor
Robotics and Computer Technology Lab, University of Seville, Seville, Spain
Interests: deep learning; computer vision; e-Health; embedded systems; neuromorphic engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, we have been able to verify the ease with which a disease can spread throughout the world causing great damage. The main way to stop the spread of these diseases is to have fast and cheap diagnostic mechanisms.This also applies to other diseases, which require fast and efficient diagnostic mechanisms. With the evolution of technology, it is common to find multiple devices and machines that help in obtaining information from patients: images and/or physiological data. However, the lack of professionals prevents these data from being evaluated and, in certain circumstances, diagnoses are significantly delayed. Therefore, it is important to design and implement deep learning systems that are capable of serving as diagnostic aid mechanisms, working with the information provided by all these capture sensors.

This Special Issue will create a showcase to increase the visibility of works related to the analysis of medical information collected from all types of sensors: from medical imaging systems to wearable sensors attached to the patient.

Dr. Manuel Dominguez-Morales
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

  • machine learning
  • deep learning
  • physiological data
  • wearable sensors
  • e-Health
  • medical imaging

Published Papers (3 papers)

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Research

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22 pages, 18075 KiB  
Article
Smart Shoe Insole Based on Polydimethylsiloxane Composite Capacitive Sensors
by Francisco Luna-Perejón, Blas Salvador-Domínguez, Fernando Perez-Peña, José María Rodríguez Corral, Elena Escobar-Linero and Arturo Morgado-Estévez
Sensors 2023, 23(3), 1298; https://doi.org/10.3390/s23031298 - 23 Jan 2023
Cited by 6 | Viewed by 3015
Abstract
Nowadays, the study of the gait by analyzing the distribution of plantar pressure is a well-established technique. The use of intelligent insoles allows real-time monitoring of the user. Thus, collecting and analyzing information is a more accurate process than consultations in so-called gait [...] Read more.
Nowadays, the study of the gait by analyzing the distribution of plantar pressure is a well-established technique. The use of intelligent insoles allows real-time monitoring of the user. Thus, collecting and analyzing information is a more accurate process than consultations in so-called gait laboratories. Most of the previous published studies consider the composition and operation of these insoles based on resistive sensors. However, the use of capacitive sensors could provide better results, in terms of linear behavior under the pressure exerted. This behavior depends on the properties of the dielectric used. In this work, the design and implementation of an intelligent plantar insole composed of capacitive sensors is proposed. The dielectric used is a polydimethylsiloxane (PDMS)-based composition. The sensorized plantar insole developed achieves its purpose as a tool for collecting pressure in different areas of the sole of the foot. The fundamentals and details of the composition, manufacture, and implementation of the insole and the system used to collect data, as well as the data samples, are shown. Finally, a comparison of the behavior of both insoles, resistive and capacitive sensor-equipped, is made. The prototype presented lays the foundation for the development of a tool to support the diagnosis of gait abnormalities. Full article
(This article belongs to the Special Issue Smart Sensors for Medical Data Acquisition and Analysis)
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11 pages, 1794 KiB  
Article
Machine Learning-Based Classification of Abnormal Liver Tissues Using Relative Permittivity
by Poulami Samaddar, Anup Kumar Mishra, Sunil Gaddam, Mansunderbir Singh, Vaishnavi K. Modi, Keerthy Gopalakrishnan, Rachel L. Bayer, Ivone Cristina Igreja Sa, Shalil Khanal, Petra Hirsova, Enis Kostallari, Shuvashis Dey, Dipankar Mitra, Sayan Roy and Shivaram P. Arunachalam
Sensors 2022, 22(24), 9919; https://doi.org/10.3390/s22249919 - 16 Dec 2022
Cited by 6 | Viewed by 2307
Abstract
The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ [...] Read more.
The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools. Full article
(This article belongs to the Special Issue Smart Sensors for Medical Data Acquisition and Analysis)
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Review

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23 pages, 808 KiB  
Review
Wearable Health Devices for Diagnosis Support: Evolution and Future Tendencies
by Elena Escobar-Linero, Luis Muñoz-Saavedra, Francisco Luna-Perejón, José Luis Sevillano and Manuel Domínguez-Morales
Sensors 2023, 23(3), 1678; https://doi.org/10.3390/s23031678 - 3 Feb 2023
Cited by 6 | Viewed by 4693
Abstract
The use of wearable devices has increased substantially in recent years. This, together with the rise of telemedicine, has led to the use of these types of devices in the healthcare field. In this work, we carried out a detailed study on the [...] Read more.
The use of wearable devices has increased substantially in recent years. This, together with the rise of telemedicine, has led to the use of these types of devices in the healthcare field. In this work, we carried out a detailed study on the use of these devices (regarding the general trends); we analyzed the research works and devices marketed in the last 10 years. This analysis extracted relevant information on the general trend of use, as well as more specific aspects, such as the use of sensors, communication technologies, and diseases. A comparison was made between the commercial and research aspects linked to wearables in the healthcare field, and upcoming trends were analyzed. Full article
(This article belongs to the Special Issue Smart Sensors for Medical Data Acquisition and Analysis)
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