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Selected Papers from 2022 and 2023 IEEE International Conference on e-Health and Bioengineering

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 20686

Special Issue Editors

Prof. Dr. Hariton-Nicolae Costin

E-Mail Website
Guest Editor
Institute of Computer Science of the Romanian Academy, Iasi Branch, 700481 Iasi, Romania
Interests: biosignal processing; biomedical image processing; artificial intelligence (neural networks, fuzzy systems, bio-inspired algorithms); (bio)sensors/transducers; e-health and telemedicine; assistive technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Cristian Rotariu

E-Mail Website
Guest Editor
Faculty of Medical Bioengineering, Grigore T. Popa University of Medicine and Pharmacy of Iași, 9-13 Kogalniceanu str., 700454 Iași, Romania
Interests: biomedical signal and medical image processing; telemedicine; assistive technologies; wearable medical sensors and devices
Special Issues, Collections and Topics in MDPI journals
Dr. Monica Fira

E-Mail Website
Guest Editor
Institute of Computer Science of the Romanian Academy, Iasi Branch, 700481 Iasi, Romania
Interests: biomedical signal; biosignal processing; compressed sensed; classifications; data compression

Special Issue Information

Dear Colleagues,

This Special Issue is connected to the 10th IEEE International Conference on e-Health and Bioengineering (EHB 2022 http://www.ehbconference.ro/Home.aspx). We welcome extended versions of submissions from the conference and regular submissions within this field.

The main objective of EHB 2022 is to cover a broad spectrum of up-to-date topics of e-Health and Medical Bioengineering/Biomedical Engineering by giving the opportunity for scientists from diverse fields to participate in the presentation, discussion and evaluation of the latest advances, research challenges, and opportunities in hardware/software technologies, medical devices/instrumentation, biosignal and image processing, biomaterials, biomechanics, biotechnologies, bioinformatics, micro and nanotechnologies, systems biology or virtual physiological human. The topics include but are not limited to:

  • Medical informatics
  • Medical robotics and actuators
  • Medical records; clinical, epidemiological and multimedia databases
  • Medical imaging, image processing and analysis
  • Biosignal processing
  • Hospital information systems and healthcare networks
  • Telemedicine, e-health and telecommunications
  • Wearable systems and sensors, m-health and p-health systems
  • Internet in healthcare and medical web portals
  • Cloud computing
  • Decision support systems and artificial intelligence in medicine
  • Bioinformatics
  • Internet and network applications
  • Wireless sensor networks
  • Laser technology and optical communication
  • Mathematics for healthcare
  • Microelectronics
  • Healthcare in the space environment
  • Healthcare in military domain
  • Optoelectronics for health
  • Renewable energy sources for medical devices and healthcare
  • Embedded systems
  • Computational biology
  • Biomechanics
  • Biomaterials
  • Micro and nanotechnology for medicine
  • Medical physics and biophysics
  • Chemistry applied in medicine
  • Medical devices and equipment
  • Measurement and instrumentation in bioengineering
  • Biometrics, forensics and security
  • Health technology assessment
  • Rehabilitative and assistive technologies
  • Electromagnetic compatibility
  • Biotechnologies
  • Instrumental analysis and laboratory technologies
  • Molecular bioengineering
  • Bioengineering in dental and oral health
  • Multimedia applications for medical and healthcare education and e-learning
  • Systems biology
  • Modeling and simulation methodologies
  • Environmental protection and management. Impacts on health and safety
  • Virtual physiological human
  • Neurosciences
  • Economics and health informatics
  • Management and marketing in healthcare
  • Psychology and health
  • Ethical and legal aspects of e-health and telemedicine
  • Biomedical sciences communication and career development
  • Innovation, development and interdisciplinary research

Prof. Dr. Hariton-Nicolae Costin
Dr. Cristian Rotariu
Dr. Monica Fira
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.

Published Papers (6 papers)

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16 pages, 7222 KiB  
Article
An Optimized Stimulation Control System for Upper Limb Exoskeleton Robot-Assisted Rehabilitation Using a Fuzzy Logic-Based Pain Detection Approach
by Ismail Ben Abdallah and Yassine Bouteraa
Sensors 2024, 24(4), 1047; https://doi.org/10.3390/s24041047 - 06 Feb 2024
Viewed by 687
Abstract
The utilization of robotic systems in upper limb rehabilitation has shown promising results in aiding individuals with motor impairments. This research introduces an innovative approach to enhance the efficiency and adaptability of upper limb exoskeleton robot-assisted rehabilitation through the development of an optimized [...] Read more.
The utilization of robotic systems in upper limb rehabilitation has shown promising results in aiding individuals with motor impairments. This research introduces an innovative approach to enhance the efficiency and adaptability of upper limb exoskeleton robot-assisted rehabilitation through the development of an optimized stimulation control system (OSCS). The proposed OSCS integrates a fuzzy logic-based pain detection approach designed to accurately assess and respond to the patient’s pain threshold during rehabilitation sessions. By employing fuzzy logic algorithms, the system dynamically adjusts the stimulation levels and control parameters of the exoskeleton, ensuring personalized and optimized rehabilitation protocols. This research conducts comprehensive evaluations, including simulation studies and clinical trials, to validate the OSCS’s efficacy in improving rehabilitation outcomes while prioritizing patient comfort and safety. The findings demonstrate the potential of the OSCS to revolutionize upper limb exoskeleton-assisted rehabilitation by offering a customizable and adaptive framework tailored to individual patient needs, thereby advancing the field of robotic-assisted rehabilitation. Full article
(This article belongs to the Special Issue Selected Papers from 2022 and 2023 IEEE International Conference on e-Health and Bioengineering)
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28 pages, 24224 KiB  
Article
Captive Animal Behavior Study by Video Analysis
by Florin Rotaru, Silviu-Ioan Bejinariu, Hariton-Nicolae Costin, Ramona Luca and Cristina Diana Niţă
Sensors 2023, 23(18), 7928; https://doi.org/10.3390/s23187928 - 16 Sep 2023
Viewed by 868
Abstract
Three video analysis-based applications for the study of captive animal behavior are presented. The aim of the first one is to provide certain parameters to assess drug efficiency by analyzing the movement of a rat. The scene is a three-chamber plastic box. First, [...] Read more.
Three video analysis-based applications for the study of captive animal behavior are presented. The aim of the first one is to provide certain parameters to assess drug efficiency by analyzing the movement of a rat. The scene is a three-chamber plastic box. First, the rat can move only in the middle room. The rat’s head pose is the first parameter needed. Secondly, the rodent could walk in all three compartments. The entry number in each area and visit duration are the other indicators used in the final evaluation. The second application is related to a neuroscience experiment. Besides the electroencephalographic (EEG) signals yielded by a radio frequency link from a headset mounted on a monkey, the head placement is a useful source of information for reliable analysis, as well as its orientation. Finally, a fusion method to construct the displacement of a panda bear in a cage and the corresponding motion analysis to recognize its stress states are shown. The arena is a zoological garden that imitates the native environment of a panda bear. This surrounding is monitored by means of four video cameras. We have applied the following stages: (a) panda detection for every video camera; (b) panda path construction from all routes; and (c) panda way filtering and analysis. Full article
(This article belongs to the Special Issue Selected Papers from 2022 and 2023 IEEE International Conference on e-Health and Bioengineering)
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16 pages, 3162 KiB  
Article
Monitoring of Cardiorespiratory Parameters during Sleep Using a Special Holder for the Accelerometer Sensor
by Andrei Boiko, Maksym Gaiduk, Wilhelm Daniel Scherz, Andrea Gentili, Massimo Conti, Simone Orcioni, Natividad Martínez Madrid and Ralf Seepold
Sensors 2023, 23(11), 5351; https://doi.org/10.3390/s23115351 - 05 Jun 2023
Cited by 3 | Viewed by 1703
Abstract
Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and [...] Read more.
Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and accurately measure cardiorespiratory parameters, is of great interest. The aim of this study is to validate a non-invasive and unobtrusive cardiorespiratory parameter monitoring system based on an accelerometer sensor. This system includes a special holder to install the system under the bed mattress. The additional aim is to determine the optimum relative system position (in relation to the subject) at which the most accurate and precise values of measured parameters could be achieved. The data were collected from 23 subjects (13 males and 10 females). The obtained ballistocardiogram signal was sequentially processed using a sixth-order Butterworth bandpass filter and a moving average filter. As a result, an average error (compared to reference values) of 2.24 beats per minute for heart rate and 1.52 breaths per minute for respiratory rate was achieved, regardless of the subject’s sleep position. For males and females, the errors were 2.28 bpm and 2.19 bpm for heart rate and 1.41 rpm and 1.30 rpm for respiratory rate. We determined that placing the sensor and system at chest level is the preferred configuration for cardiorespiratory measurement. Further studies of the system’s performance in larger groups of subjects are required, despite the promising results of the current tests in healthy subjects. Full article
(This article belongs to the Special Issue Selected Papers from 2022 and 2023 IEEE International Conference on e-Health and Bioengineering)
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18 pages, 3644 KiB  
Article
COVID-19 Detection from Cough Recordings Using Bag-of-Words Classifiers
by Irina Pavel and Iulian B. Ciocoiu
Sensors 2023, 23(11), 4996; https://doi.org/10.3390/s23114996 - 23 May 2023
Cited by 2 | Viewed by 1044
Abstract
Reliable detection of COVID-19 from cough recordings is evaluated using bag-of-words classifiers. The effect of using four distinct feature extraction procedures and four different encoding strategies is evaluated in terms of the Area Under Curve (AUC), accuracy, sensitivity, and F1-score. Additional studies include [...] Read more.
Reliable detection of COVID-19 from cough recordings is evaluated using bag-of-words classifiers. The effect of using four distinct feature extraction procedures and four different encoding strategies is evaluated in terms of the Area Under Curve (AUC), accuracy, sensitivity, and F1-score. Additional studies include assessing the effect of both input and output fusion approaches and a comparative analysis against 2D solutions using Convolutional Neural Networks. Extensive experiments conducted on the COUGHVID and COVID-19 Sounds datasets indicate that sparse encoding yields the best performances, showing robustness against various combinations of feature type, encoding strategy, and codebook dimension parameters. Full article
(This article belongs to the Special Issue Selected Papers from 2022 and 2023 IEEE International Conference on e-Health and Bioengineering)
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19 pages, 4280 KiB  
Article
Sound-Based Localization Using LSTM Networks for Visually Impaired Navigation
by Mohsen Bakouri, Naif Alyami, Ahmad Alassaf, Mohamed Waly, Tariq Alqahtani, Ibrahim AlMohimeed, Abdulrahman Alqahtani, Md Samsuzzaman, Husham Farouk Ismail and Yousef Alharbi
Sensors 2023, 23(8), 4033; https://doi.org/10.3390/s23084033 - 17 Apr 2023
Cited by 1 | Viewed by 2715
Abstract
In this work, we developed a prototype that adopted sound-based systems for localization of visually impaired individuals. The system was implemented based on a wireless ultrasound network, which helped the blind and visually impaired to navigate and maneuver autonomously. Ultrasonic-based systems use high-frequency [...] Read more.
In this work, we developed a prototype that adopted sound-based systems for localization of visually impaired individuals. The system was implemented based on a wireless ultrasound network, which helped the blind and visually impaired to navigate and maneuver autonomously. Ultrasonic-based systems use high-frequency sound waves to detect obstacles in the environment and provide location information to the user. Voice recognition and long short-term memory (LSTM) techniques were used to design the algorithms. The Dijkstra algorithm was also used to determine the shortest distance between two places. Assistive hardware tools, which included an ultrasonic sensor network, a global positioning system (GPS), and a digital compass, were utilized to implement this method. For indoor evaluation, three nodes were localized on the doors of different rooms inside the house, including the kitchen, bathroom, and bedroom. The coordinates (interactive latitude and longitude points) of four outdoor areas (mosque, laundry, supermarket, and home) were identified and stored in a microcomputer’s memory to evaluate the outdoor settings. The results showed that the root mean square error for indoor settings after 45 trials is about 0.192. In addition, the Dijkstra algorithm determined that the shortest distance between two places was within an accuracy of 97%. Full article
(This article belongs to the Special Issue Selected Papers from 2022 and 2023 IEEE International Conference on e-Health and Bioengineering)
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36 pages, 1277 KiB  
Systematic Review
Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review
by Mohammad Moshawrab, Mehdi Adda, Abdenour Bouzouane, Hussein Ibrahim and Ali Raad
Sensors 2023, 23(2), 828; https://doi.org/10.3390/s23020828 - 11 Jan 2023
Cited by 18 | Viewed by 8114
Abstract
Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use [...] Read more.
Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use. Full article
(This article belongs to the Special Issue Selected Papers from 2022 and 2023 IEEE International Conference on e-Health and Bioengineering)
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