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Bioengineering
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IoT Technology in Bioengineering Applications
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IoT Technology in Bioengineering Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 8605

Special Issue Editors

Dr. Mihaela Hnatiuc

E-Mail Website
Guest Editor
Electronic and Telecommunication Departament, Constanta Maritime University, 104 Mircea cel Batran, 900663 Constanta, Romania
Interests: electronic embedded systems; intelligent sensors and interface; smart home; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Larbi Boubchir

E-Mail Website
Guest Editor
LIASD Research Lab, Department of Computer Science, University of Paris, 8, 93526 Saint-Denis, France
Interests: biomedical signal processing; EEG; image processing; machine learning; brain–computer interface; biometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue presents novel solutions to challenging real-world problems applying IoT devices to bioengineering. IoT technology is used in therapies, implants, diagnostics, adaptive prosthetics, etc., where data are recorded and processed in the cloud for Internet-based uses. This method was developed for remote monitoring to improve people's lives. At the same time, eco plants and biofoods greatly impact human health. IoT technology is used to monitor and diagnose farms and food to improve the nutrient and food quality.

The "IoT Technology in Bioengineering Applications" issue publishes research using quantitative tools, including simulation and mathematical modeling. This Special Issue focuses on exciting applications for bioengineering science in health, medicine, and agronomy.

Dr. Mihaela Hnatiuc
Prof. Dr. Larbi Boubchir
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • robotic device
  • signal processing
  • image processing
  • communication protocol
  • embedded system
  • smart sensors
  • cloud/FOG
  • predictive methods
  • monitoring
  • process optimization
  • diagnosis
  • implant
  • tele surgery
  • teleconsultation
  • telemonitoring

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Published Papers (6 papers)

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Research

15 pages, 1096 KiB  
Article
Substantiation and Effectiveness of Remote Monitoring System Based on IoMT Using Portable ECG Device
by Hee-Young Lee, Yoon-Ji Kim, Kang-Hyun Lee, Jung-Hun Lee, Sung-Pil Cho, Junghwan Park, Il-Hwan Park and Hyun Youk
Bioengineering 2024, 11(8), 836; https://doi.org/10.3390/bioengineering11080836 - 16 Aug 2024
Viewed by 618
Abstract
Cardiovascular disease is a major global health concern, with early detection being critical. This study assesses the effectiveness of a portable ECG device, based on Internet of Medical Things (IoMT) technology, for remote cardiovascular monitoring during daily activities. We conducted a clinical trial [...] Read more.
Cardiovascular disease is a major global health concern, with early detection being critical. This study assesses the effectiveness of a portable ECG device, based on Internet of Medical Things (IoMT) technology, for remote cardiovascular monitoring during daily activities. We conducted a clinical trial involving 2000 participants who wore the HiCardi device while engaging in hiking activities. The device monitored their ECG, heart rate, respiration, and body temperature in real-time. If an abnormal signal was detected while a physician was remotely monitoring the ECG at the IoMT monitoring center, he notified the clinical research coordinator (CRC) at the empirical research site, and the CRC advised the participant to visit a hospital. Follow-up calls were made to determine compliance and outcomes. Of the 2000 participants, 318 showed abnormal signals, and 182 were advised to visit a hospital. The follow-up revealed that 139 (76.37%) responded, and 30 (21.58% of those who responded) sought further medical examination. Most visits (80.00%) occurred within one month. Diagnostic approaches included ECG (56.67%), ECG and ultrasound (20.00%), ultrasound alone (16.67%), ECG and X-ray (3.33%), and general treatment (3.33%). Seven participants (23.33% of those who visited) were diagnosed with cardiovascular disease, including conditions such as arrhythmia, atrial fibrillation, and stent requirements. The portable ECG device using the patch-type electrocardiograph detected abnormal cardiovascular signals, leading to timely diagnoses and interventions, demonstrating its potential for broad applications in preventative healthcare. Full article
(This article belongs to the Special Issue IoT Technology in Bioengineering Applications)
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17 pages, 2684 KiB  
Article
Characterization of Cochlear Implant Artifact and Removal Based on Multi-Channel Wiener Filter in Unilateral Child Patients
by Dario Rossi, Giulia Cartocci, Bianca M. S. Inguscio, Giulia Capitolino, Gianluca Borghini, Gianluca Di Flumeri, Vincenzo Ronca, Andrea Giorgi, Alessia Vozzi, Rossella Capotorto, Fabio Babiloni, Alessandro Scorpecci, Sara Giannantonio, Pasquale Marsella, Carlo Antonio Leone, Rosa Grassia, Francesco Galletti, Francesco Ciodaro, Cosimo Galletti and Pietro Aricò
Bioengineering 2024, 11(8), 753; https://doi.org/10.3390/bioengineering11080753 - 24 Jul 2024
Viewed by 668
Abstract
Cochlear implants (CI) allow deaf patients to improve language perception and improving their emotional valence assessment. Electroencephalographic (EEG) measures were employed so far to improve CI programming reliability and to evaluate listening effort in auditory tasks, which are particularly useful in conditions when [...] Read more.
Cochlear implants (CI) allow deaf patients to improve language perception and improving their emotional valence assessment. Electroencephalographic (EEG) measures were employed so far to improve CI programming reliability and to evaluate listening effort in auditory tasks, which are particularly useful in conditions when subjective evaluations are scarcely appliable or reliable. Unfortunately, the presence of CI on the scalp introduces an electrical artifact coupled to EEG signals that masks physiological features recorded by electrodes close to the site of implant. Currently, methods for CI artifact removal have been developed for very specific EEG montages or protocols, while others require many scalp electrodes. In this study, we propose a method based on the Multi-channel Wiener filter (MWF) to overcome those shortcomings. Nine children with unilateral CI and nine age-matched normal hearing children (control) participated in the study. EEG data were acquired on a relatively low number of electrodes (n = 16) during resting condition and during an auditory task. The obtained results obtained allowed to characterize CI artifact on the affected electrode and to significantly reduce, if not remove it through MWF filtering. Moreover, the results indicate, by comparing the two sample populations, that the EEG data loss is minimal in CI users after filtering, and that data maintain EEG physiological characteristics. Full article
(This article belongs to the Special Issue IoT Technology in Bioengineering Applications)
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19 pages, 3044 KiB  
Article
Reshaping Bioacoustics Event Detection: Leveraging Few-Shot Learning (FSL) with Transductive Inference and Data Augmentation
by Nouman Ijaz, Farhad Banoori and Insoo Koo
Bioengineering 2024, 11(7), 685; https://doi.org/10.3390/bioengineering11070685 - 5 Jul 2024
Viewed by 1011
Abstract
Bioacoustic event detection is a demanding endeavor involving recognizing and classifying the sounds animals make in their natural habitats. Traditional supervised learning requires a large amount of labeled data, which are hard to come by in bioacoustics. This paper presents a few-shot learning [...] Read more.
Bioacoustic event detection is a demanding endeavor involving recognizing and classifying the sounds animals make in their natural habitats. Traditional supervised learning requires a large amount of labeled data, which are hard to come by in bioacoustics. This paper presents a few-shot learning (FSL) method incorporating transductive inference and data augmentation to address the issues of too few labeled events and small volumes of recordings. Here, transductive inference iteratively alters class prototypes and feature extractors to seize essential patterns, whereas data augmentation applies SpecAugment on Mel spectrogram features to augment training data. The proposed approach is evaluated by using the Detecting and Classifying Acoustic Scenes and Events (DCASE) 2022 and 2021 datasets. Extensive experimental results demonstrate that all components of the proposed method achieve significant F-score improvements of 27% and 10%, for the DCASE-2022 and DCASE-2021 datasets, respectively, compared to recent advanced approaches. Moreover, our method is helpful in FSL tasks because it effectively adapts to sounds from various animal species, recordings, and durations. Full article
(This article belongs to the Special Issue IoT Technology in Bioengineering Applications)
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19 pages, 5601 KiB  
Article
Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning
by Rehan Khan, Shafi Ullah Khan, Umer Saeed and In-Soo Koo
Bioengineering 2024, 11(6), 586; https://doi.org/10.3390/bioengineering11060586 - 8 Jun 2024
Cited by 1 | Viewed by 937
Abstract
Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the [...] Read more.
Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the interpretation of lung sounds is a subjective and labor-intensive process that demands considerable medical expertise, and there is a good chance of misclassification. To address this problem, we propose a hybrid deep learning technique that incorporates signal processing techniques. Parallel transformation is applied to adventitious respiratory sounds, transforming lung sound signals into two distinct time-frequency scalograms: the continuous wavelet transform and the mel spectrogram. Furthermore, parallel convolutional autoencoders are employed to extract features from scalograms, and the resulting latent space features are fused into a hybrid feature pool. Finally, leveraging a long short-term memory model, a feature from the latent space is used as input for classifying various types of respiratory diseases. Our work is evaluated using the ICBHI-2017 lung sound dataset. The experimental findings indicate that our proposed method achieves promising predictive performance, with average values for accuracy, sensitivity, specificity, and F1-score of 94.16%, 89.56%, 99.10%, and 89.56%, respectively, for eight-class respiratory diseases; 79.61%, 78.55%, 92.49%, and 78.67%, respectively, for four-class diseases; and 85.61%, 83.44%, 83.44%, and 84.21%, respectively, for binary-class (normal vs. abnormal) lung sounds. Full article
(This article belongs to the Special Issue IoT Technology in Bioengineering Applications)
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16 pages, 3770 KiB  
Article
Intelligent Grapevine Disease Detection Using IoT Sensor Network
by Mihaela Hnatiuc, Simona Ghita, Domnica Alpetri, Aurora Ranca, Victoria Artem, Ionica Dina, Mădălina Cosma and Mazin Abed Mohammed
Bioengineering 2023, 10(9), 1021; https://doi.org/10.3390/bioengineering10091021 - 29 Aug 2023
Cited by 10 | Viewed by 1575
Abstract
The Internet of Things (IoT) has gained significance in agriculture, using remote sensing and machine learning to help farmers make high-precision management decisions. This technology can be applied in viticulture, making it possible to monitor disease occurrence and prevent them automatically. The study [...] Read more.
The Internet of Things (IoT) has gained significance in agriculture, using remote sensing and machine learning to help farmers make high-precision management decisions. This technology can be applied in viticulture, making it possible to monitor disease occurrence and prevent them automatically. The study aims to achieve an intelligent grapevine disease detection method, using an IoT sensor network that collects environmental and plant-related data. The focus of this study is the identification of the main parameters which provide early information regarding the grapevine’s health. An overview of the sensor network, architecture, and components is provided in this paper. The IoT sensors system is deployed in the experimental plots located within the plantations of the Research Station for Viticulture and Enology (SDV) in Murfatlar, Romania. Classical methods for disease identification are applied in the field as well, in order to compare them with the sensor data, thus improving the algorithm for grapevine disease identification. The data from the sensors are analyzed using Machine Learning (ML) algorithms and correlated with the results obtained using classical methods in order to identify and predict grapevine diseases. The results of the disease occurrence are presented along with the corresponding environmental parameters. The error of the classification system, which uses a feedforward neural network, is 0.05. This study will be continued with the results obtained from the IoT sensors tested in vineyards located in other regions. Full article
(This article belongs to the Special Issue IoT Technology in Bioengineering Applications)
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26 pages, 5282 KiB  
Article
Securing Group Patient Communication in 6G-Aided Dynamic Ubiquitous Healthcare with Real-Time Mobile DNA Sequencing
by Tuan-Vinh Le
Bioengineering 2023, 10(7), 839; https://doi.org/10.3390/bioengineering10070839 - 15 Jul 2023
Viewed by 1879
Abstract
(1) Background: With an advanced technique, third-generation sequencing (TGS) provides services with long deoxyribonucleic acid (DNA) reads and super short sequencing time. It enables onsite mobile DNA sequencing solutions for enabling ubiquitous healthcare (U-healthcare) services with modern mobile technology and smart entities in [...] Read more.
(1) Background: With an advanced technique, third-generation sequencing (TGS) provides services with long deoxyribonucleic acid (DNA) reads and super short sequencing time. It enables onsite mobile DNA sequencing solutions for enabling ubiquitous healthcare (U-healthcare) services with modern mobile technology and smart entities in the internet of living things (IoLT). Due to some strict requirements, 6G technology can efficiently facilitate communications in a truly intelligent U-healthcare IoLT system. (2) Research problems: conventional single user–server architecture is not able to enable group conversations where “multiple patients–server” communication or “patient–patient” communication in the group is required. The communications are carried out via the open Internet, which is not a trusted channel. Since heath data and medical information are very sensitive, security and privacy concerns in the communication systems have become extremely important. (3) Purpose: the author aims to propose a dynamic group-based patient-authenticated key distribution protocol for 6G-aided U-healthcare services enabled by mobile DNA sequencing. In the protocol, an authenticated common session key is distributed by the server to the patients. Using the key, patients in a healthcare group are allowed to securely connect with the service provider or with each other for specific purposes of communication. (4) Results: the group key distribution process is protected by a secure three-factor authentication mechanism along with an efficient sequencing-device-based single sign-on (SD-SSO) solution. Based on traceable information stored in the server database, the proposed approach can provide patient-centered services which are available on multiple mobile devices. Security robustness of the proposed protocol is proven by well-known verification tools and a detailed semantic discussion. Performance evaluation shows that the protocol provides more functionality and incurs a reasonable overhead in comparison with the existing works. Full article
(This article belongs to the Special Issue IoT Technology in Bioengineering Applications)
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