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Special Issue Information

Dear Colleagues,

Today, especially due to the COVID-19 pandemic, many new telemedicine services have been launched. This affects many medical specialties, and from a technological point of view, it can include the use of sensors that allow the quantification of biological values that can help doctors to diagnose or miniaturize a patient’s condition. In the particular case of rehabilitation activities, sensors play a key role in the evaluation and correction of these activities in real time.

In this framework, it is our pleasure to edit this Special Issue on “Sensors for Rehabilitation, Telemedicine, and Assistive Technology”.

The Special Issue is dedicated to presenting robust remote sensing data gathering, advanced artificial intelligence, and sound analysis procedures for deriving meaningful outcomes. Additionally, the use of innovative techniques such as quantum computing or block chain will be welcome if applied to telemedicine, rehabilitation or assistive technologies.

Prof. Dr. Begoña Garcia-Zapirain
Prof. Dr. Adel Elmaghraby
Dr. Daniel Sierra-Sosa
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

Artificial intelligence
Deep learning
Machine learning
Data and signal processing
Parallel computing
Explainable AI
Healthcare
Smart cities
Internet of Things (IoT)
Healthcare privacy and security
Medical image processing

Published Papers (2 papers)

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Research

Jump to: Review

18 pages, 6517 KiB

Open AccessArticle

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach

by Rouhollah Kian Ara, Andrzej Matiolański, Andrzej Dziech, Remigiusz Baran, Paweł Domin and Adam Wieczorkiewicz

Sensors 2022, 22(13), 4675; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134675 - 21 Jun 2022

Cited by 6 | Viewed by 2465

Abstract

The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with modern machine learning techniques. The main contribution is to present a new approach for the classification of eye diseases using the convolutional neural network model. The research concerns the classification of patients on the basis of OCT B-scans into one of four categories: Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Drusen, and Normal. Those categories are available in a publicly available dataset of above 84,000 images utilized for the research. After several tested architectures, our 5-layer neural network gives us a promising result. We compared them to the other available solutions which proves the high quality of our algorithm. Equally important for the application of the algorithm is the computational time, which is reduced by the limited size of the model. In addition, the article presents a detailed method of image data augmentation and its impact on the classification results. The results of the experiments were also presented for several derived models of convolutional network architectures that were tested during the research. Improving processes in medical treatment is important. The algorithm cannot replace a doctor but, for example, can be a valuable tool for speeding up the process of diagnosis during screening tests. Full article

(This article belongs to the Special Issue Sensors for Rehabilitation, Telemedicine and Assistive Technology)

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Review

Jump to: Research

27 pages, 429 KiB

Open AccessReview

Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models

by Babak Joze Abbaschian, Daniel Sierra-Sosa and Adel Elmaghraby

Sensors 2021, 21(4), 1249; https://0-doi-org.brum.beds.ac.uk/10.3390/s21041249 - 10 Feb 2021

Cited by 157 | Viewed by 20786

Abstract

The advancements in neural networks and the on-demand need for accurate and near real-time Speech Emotion Recognition (SER) in human–computer interactions make it mandatory to compare available methods and databases in SER to achieve feasible solutions and a firmer understanding of this open-ended problem. The current study reviews deep learning approaches for SER with available datasets, followed by conventional machine learning techniques for speech emotion recognition. Ultimately, we present a multi-aspect comparison between practical neural network approaches in speech emotion recognition. The goal of this study is to provide a survey of the field of discrete speech emotion recognition. Full article

(This article belongs to the Special Issue Sensors for Rehabilitation, Telemedicine and Assistive Technology)

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