Applied System on Biomedical Engineering, Healthcare and Sustainability 2023

Topic Information

Dear Colleagues,

Recently, healthcare is undergoing a sector-wide transformation thanks to advances in computing, networking technologies, big data, and artificial intelligence. Healthcare is not only changing from reactive and hospital-centered to preventive and personalized, but is also changing from disease focused to well-being-centered (https://www.ecbios.asia/). Healthcare systems, as well as fundamental medicine research, are becoming smarter and utilizing biomedical engineering. Furthermore, with cutting edge sensors and computer technologies, healthcare delivery could also yield better efficiency, higher quality, and lower cost. However, these innovations often do not lead to sustainability, health, and happiness for all people. Science and technology are to be complemented by the arts, humanities, social sciences, and indigenous know-how and wisdom in order to increase the accessibility of the benefits for the needy across all regions and classes of people. We need ethically aligned and driven health care systems and sustainability. This topic “Applied System on Biomedical Engineering, Healthcare and Sustainability 2023” includes five journals, Journal of Clinical Medicine, Applied Sciences, Electronics, Bioengineering, and Healthcare, which aim to publish excellent papers about relative fields. This enables an interdisciplinary collaboration between science and engineering technologists in the academic and industrial fields, as well as internaional networking.

Topics of interest include the following:

• Smart healthcare system analysis and design;
• Computer and human–machine interactions in the healthcare system;
• Application of Internet of Things (IoT) in the healthcare system;
• Big-data- and artificial-intelligence-enabled healthcare systems;
• Health-related aspects of sustainability;
• Environmental education and public health;
• Environmental engineering and biotechnology rehabilitation medicine and physiotherapy;
• Sports medicine;
• Pediatric and geriatric emergency care;
• Leisure and recreation;
• Health promotion;
• Nourishment and health care;
• Disaster and health;
• Health and the environment;
• Health services;
• Occupational health;
• Impact of safety, security and disaster management on sustainability;
• Sustainability science;
• Medical electronics;
• Biomedical materials;
• Biomedical diagnostic techniques;
• Medical information and rehabilitation technology;
• Other related topics regarding healthcare, sustainability and biomedical engineering.

Prof. Dr. Teen-­Hang Meen
Prof. Dr. Chun-Yen Chang
Prof. Dr. Charles Tijus
Prof. Dr. Po-Lei Lee
Prof. Dr. Kuei-Shu Hsu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400
Bioengineering bioengineering	4.6	4.2	2014	17.7 Days	CHF 2700
Electronics electronics	2.9	4.7	2012	15.6 Days	CHF 2400
Healthcare healthcare	2.8	2.7	2013	19.5 Days	CHF 2700
Journal of Clinical Medicine jcm	3.9	5.4	2012	17.9 Days	CHF 2600
Chips chips	-	-	2022	15.0 days *	CHF 1000

* Median value for all MDPI journals in the second half of 2023.

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Published Papers (31 papers)

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12 pages, 1874 KiB  

Open AccessArticle

Morphology Observation of Two-Dimensional Monolayers of Model Proteins on Water Surface as Revealed by Dropping Method

by Yukie Asada, Shinya Tanaka, Hirotaka Nagano, Hiroki Noguchi, Akihiro Yoshino, Keijiro Taga, Yasushi Yamamoto and Zameer Shervani

Bioengineering 2024, 11(4), 366; https://doi.org/10.3390/bioengineering11040366 - 11 Apr 2024

Viewed by 297

Abstract

We have investigated the morphology of two-dimensional monolayers of gramicidin-D (GD) and alamethicin (Al) formed on the water surface by the dropping method (DM) using surface tension measurement (STm), Brewster angle microscopy (BAM), and atomic force microscopy (AFM). Dynamic light scattering (DLS) revealed [...] Read more.

We have investigated the morphology of two-dimensional monolayers of gramicidin-D (GD) and alamethicin (Al) formed on the water surface by the dropping method (DM) using surface tension measurement (STm), Brewster angle microscopy (BAM), and atomic force microscopy (AFM). Dynamic light scattering (DLS) revealed that GD in alcoholic solutions formed a dimeric helical structure. According to the CD and NMR spectroscopies, GD molecules existed in dimer form in methanol and lipid membrane environments. The STm results and BAM images revealed that the GD dimer monolayer was in a liquid expanded (LE) state, whereas the Al monolayer was in a liquid condensed (LC) state. The limiting molecular area (A₀) was 6.2 ± 0.5 nm² for the GD-dimer and 3.6 ± 0.5 nm² for the Al molecule. The AFM images also showed that the molecular long axes of both the GD-dimer and Al were horizontal to the water surface. The stability of each monolayer was confirmed by the time dependence of the surface pressure (π) observed using the STm method. The DM monolayer preparation method for GD-dimer and Al peptide molecules is a useful technique for revealing how the model biological membrane’s components assemble in two dimensions on the water surface. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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16 pages, 5713 KiB  

Open AccessArticle

DentalArch: AI-Based Arch Shape Detection in Orthodontics

by J. D. Tamayo-Quintero, J. B. Gómez-Mendoza and S. V. Guevara-Pérez

Appl. Sci. 2024, 14(6), 2567; https://doi.org/10.3390/app14062567 - 19 Mar 2024

Viewed by 553

Abstract

Objective: This study aims to introduce and assess a novel AI-driven tool developed for the classification of orthodontic arch shapes into square, ovoid, and tapered categories. Methods: Between 2016 and 2019, we collected 450 digital dental models. Applying our inclusion and exclusion criteria, [...] Read more.

Objective: This study aims to introduce and assess a novel AI-driven tool developed for the classification of orthodontic arch shapes into square, ovoid, and tapered categories. Methods: Between 2016 and 2019, we collected 450 digital dental models. Applying our inclusion and exclusion criteria, we refined our dataset to 50 models, ensuring a focused and detailed analysis. Plaster casts were digitized into 3D models with AutoScan-DS-EX. Three trained evaluators then measured mesiodistal and arch widths using MeshLab. The development of DentalArch was undertaken in two versions: the first version incorporates 18 input parameters, including mesiodistal widths (from the first molar to the first molar, totaling 14) and arch widths (1 intercanine, 2 interpremolar, and 1 intermolar, totaling 4); the second version uses only 4 parameters related to arch widths. Both versions aim to predict the arch shape. An evaluation of 28 machine learning methods through a k = 5-fold cross-validation was conducted to determine the most effective techniques. Results: In the tests, the performance evaluation of the DentalArch software in detecting arch shapes revealed that version 1, which analyzes 18 parameters, achieved an accuracy of 94.7% for the lower arch and 93% for the upper arch. The more streamlined version 2, which assesses only four parameters, also showed high precision with an accuracy of 93.0% for the lower arch and 92.7% for the upper arch. Conclusions: DentalArch provides a tool with potential use in orthodontic diagnostics, particularly in the task of arch shape classification. The software offers a less subjective and data-driven approach to arch shape determination. Moreover, the open-source nature of DentalArch ensures its global availability and encourages contributions from the orthodontic community. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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27 pages, 83136 KiB  

Open AccessArticle

Frequency-Dependent Contrast Enhancement for Conductive and Non-Conductive Materials in Electrical Impedance Tomography

by Quoc Tuan Nguyen Diep, Hoang Nhut Huynh, Minh Quan Cao Dinh, Thanh Ven Huynh, Anh Tu Tran and Trung Nghia Tran

Appl. Sci. 2024, 14(5), 2141; https://doi.org/10.3390/app14052141 - 04 Mar 2024

Viewed by 630

Abstract

This research investigates the critical role of frequency selection in Electrical Impedance Tomography (EIT), a non-invasive imaging technique that reconstructs internal conductivity distributions through injected electrical currents. Empirical frequency selection is paramount to maximizing the fidelity and specificity of EIT images. The study [...] Read more.

This research investigates the critical role of frequency selection in Electrical Impedance Tomography (EIT), a non-invasive imaging technique that reconstructs internal conductivity distributions through injected electrical currents. Empirical frequency selection is paramount to maximizing the fidelity and specificity of EIT images. The study explores the impact of distinct frequency ranges—low, medium, and high—on image contrast and clarity, particularly focusing on differentiating conductive materials from non-conductive materials. The findings reveal distinct empirical frequency bands for enhancing the respective contrasts: 15–38 kHz for conductive materials (copper) and 45–75 kHz for non-conductive materials (acrylic resin). These insights shed light on the frequency-dependent nature of material contrast in EIT images, guiding the selection of empirical operating ranges for various target materials. This research paves the way for improved sensitivity and broader applicability of EIT in diverse areas. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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17 pages, 3093 KiB  

Open AccessArticle

Real-Time Compact Digital Processing Chain for the Detection and Sorting of Neural Spikes from Implanted Microelectrode Arrays

by Andrea Vittimberga, Riccardo Corelli and Giuseppe Scotti

Chips 2024, 3(1), 32-48; https://doi.org/10.3390/chips3010002 - 08 Feb 2024

Viewed by 559

Abstract

Implantable microelectrodes arrays are used to record electrical signals from surrounding neurons and have led to incredible improvements in modern neuroscience research. Digital signals resulting from conditioning and the analog-to-digital conversion of neural spikes captured by microelectrodes arrays have to be elaborated in [...] Read more.

Implantable microelectrodes arrays are used to record electrical signals from surrounding neurons and have led to incredible improvements in modern neuroscience research. Digital signals resulting from conditioning and the analog-to-digital conversion of neural spikes captured by microelectrodes arrays have to be elaborated in a dedicated DSP core devoted to a real-time spike-sorting process for the classification phase based on the source neurons from which they were emitted. On-chip spike-sorting is also essential to achieve enough data reduction to allow for wireless transmission within the power constraints imposed on implantable devices. The design of such integrated circuits must meet stringent constraints related to ultra-low power density and the minimum silicon area, as well as several application requirements. The aim of this work is to present real-time hardware architecture able to perform all the spike-sorting tasks on chip while satisfying the aforementioned stringent requirements related to this type of application. The proposed solution has been coded in VHDL language and simulated in the Cadence Xcelium tool to verify the functional behavior of the digital processing chain. Then, a synthesis and place and route flow has been carried out to implement the proposed architecture in both a 130 nm and a FD-SOI 28 nm CMOS process, with a 200 MHz clock frequency target. Post-layout simulations in the Cadence Xcelium tool confirmed the proper operation up to a 200 MHz clock frequency. The area occupation and power consumption of the proposed detection and clustering module are 0.2659 mm²/ch, 7.16 μW/ch, 0.0168 mm²/ch, and 0.47 μW/ch for the 130 nm and 28 nm implementation, respectively. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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15 pages, 2294 KiB  

Open AccessArticle

Sequential Brain CT Image Captioning Based on the Pre-Trained Classifiers and a Language Model

by Jin-Woo Kong, Byoung-Doo Oh, Chulho Kim and Yu-Seop Kim

Appl. Sci. 2024, 14(3), 1193; https://doi.org/10.3390/app14031193 - 31 Jan 2024

Viewed by 702

Abstract

Intracerebral hemorrhage (ICH) is a severe cerebrovascular disorder that poses a life-threatening risk, necessitating swift diagnosis and treatment. While CT scans are the most effective diagnostic tool for detecting cerebral hemorrhage, their interpretation typically requires the expertise of skilled professionals. However, in regions [...] Read more.

Intracerebral hemorrhage (ICH) is a severe cerebrovascular disorder that poses a life-threatening risk, necessitating swift diagnosis and treatment. While CT scans are the most effective diagnostic tool for detecting cerebral hemorrhage, their interpretation typically requires the expertise of skilled professionals. However, in regions with a shortage of such experts or situations with time constraints, delays in diagnosis may occur. In this paper, we propose a method that combines a pre-trained CNN classifier and GPT-2 to generate text for sequentially acquired ICH CT images. Initially, CNN undergoes fine-tuning by learning the presence of ICH in publicly available single CT images, and subsequently, it extracts feature vectors (i.e., matrix) from 3D ICH CT images. These vectors are input along with text into GPT-2, which is trained to generate text for consecutive CT images. In experiments, we evaluated the performance of four models to determine the most suitable image captioning model: (1) In the N-gram-based method, ReseNet50V2 and DenseNet121 showed relatively high scores. (2) In the embedding-based method, DenseNet121 exhibited the best performance. (3) Overall, the models showed good performance in BERT score. Our proposed method presents an automatic and valuable approach for analyzing 3D ICH CT images, contributing to the efficiency of ICH diagnosis and treatment. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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21 pages, 3189 KiB  

Open AccessArticle

Area-Power-Delay-Efficient Multi-Modulus Multiplier Based on Area-Saving Hard Multiple Generator Using Radix-8 Booth-Encoding Scheme on Field Programmable Gate Array

by Chao-Tsung Kuo and Yao-Cheng Wu

Electronics 2024, 13(2), 311; https://doi.org/10.3390/electronics13020311 - 10 Jan 2024

Viewed by 517

Abstract

A multi-modulus architecture based on the radix-8 Booth encoding of a modulo (2ⁿ − 1) multiplier, a modulo (2ⁿ) multiplier, and a modulo (2ⁿ + 1) multiplier is proposed in this paper. It uses the original single circuit and [...] Read more.

A multi-modulus architecture based on the radix-8 Booth encoding of a modulo (2ⁿ − 1) multiplier, a modulo (2ⁿ) multiplier, and a modulo (2ⁿ + 1) multiplier is proposed in this paper. It uses the original single circuit and shares many common circuit characteristics with a small extra circuit to carry out multi-modulus operations. Compared with a previous radix-4 study, the radix-8 architecture can increase the modulation multiplication encoding selection from three codes to four codes. This reduces the use of partial products from ⌊n/2⌋ to ⌊n/3⌋ + 1, but it increases the operation complexity for multiplication by three circuits. A hard multiple generator (HMG) is used to address this problem. Two judgment signals in the multi-modulus circuit can be used to perform three operations of the modulo (2ⁿ − 1) multiplier, modulo (2ⁿ) multiplier, and modulo (2ⁿ + 1) multiplier at the same time. The weighted representation is used to reduce the number of partial products. Compared with previously reported methods in the literature, the proposed approach can achieve better performance by being more area-efficient, being faster, consuming low power, and having a lower area-delay product (ADP) and power-delay product (PDP). With the multi-modulus HMG, the proposed modified architecture can save 34.48–55.23% of hardware area. Compared with previous studies on the multi-modulus multiplier, the proposed architecture can save 22.78–35.46%, 4.12–11.15%, 12.59–24.73%, 27.88–38.88%, and 20.49–27.85% of hardware area, delay time, dissipation power, ADP, and PDP, respectively. Xilinx field programmable gate array (FPGA) Vivado 2019.2 tools and the Verilog hardware description language are used for synthesis and implementation. The Xilinx Artix-7 XC7A35T-CSG324-1 chipset is adopted to evaluate the performance. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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12 pages, 3285 KiB  

Open AccessArticle

Automatic Counting and Location Labeling of Rice Seedlings from Unmanned Aerial Vehicle Images

by Jui-Feng Yeh, Kuei-Mei Lin, Li-Ching Yuan and Jenq-Muh Hsu

Electronics 2024, 13(2), 273; https://doi.org/10.3390/electronics13020273 - 08 Jan 2024

Viewed by 764

Abstract

Traditional counting of rice seedlings in agriculture is often labor-intensive, time-consuming, and prone to errors. Therefore, agricultural automation has gradually become a prominent solution. In this paper, UVA detection, combining deep learning with unmanned aerial vehicle (UAV) sensors, contributes to precision agriculture. We [...] Read more.

Traditional counting of rice seedlings in agriculture is often labor-intensive, time-consuming, and prone to errors. Therefore, agricultural automation has gradually become a prominent solution. In this paper, UVA detection, combining deep learning with unmanned aerial vehicle (UAV) sensors, contributes to precision agriculture. We propose a YOLOv4-based approach for the counting and location marking of rice seedlings from unmanned aerial vehicle (UAV) images. The detection of tiny objects is a crucial and challenging task in agricultural imagery. Therefore, we make modifications to the data augmentation and activation functions in the neural elements of the deep learning model to meet the requirements of rice seedling detection and counting. In the preprocessing stage, we segment the UAV images into different sizes for training. Mish activation is employed to enhance the accuracy of the YOLO one-stage detector. We utilize the dataset provided in the AIdea 2021 competition to evaluate the system, achieving an F1-score of 0.91. These results indicate the superiority of the proposed method over the baseline system. Furthermore, the outcomes affirm the potential for precise detection of rice seedlings in precision agriculture. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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12 pages, 1245 KiB  

Open AccessStudy Protocol

Rationale and Design of a Wearable Cardiopulmonary Monitoring System for Improving the Efficiency of Critical Care Monitoring

by Jina Lee, You-Mi Hwang and Sung-Min Park

Appl. Sci. 2023, 13(24), 13101; https://doi.org/10.3390/app132413101 - 08 Dec 2023

Cited by 1 | Viewed by 3404

Abstract

Despite the recent development of wearable cardiopulmonary monitoring devices and their necessity in clinical settings, the evidence regarding their application in real-world intensive care units (ICUs) is limited. These devices have notable problems, such as inefficient manufacturing and cumbersome hardware for medical staff [...] Read more.

Despite the recent development of wearable cardiopulmonary monitoring devices and their necessity in clinical settings, the evidence regarding their application in real-world intensive care units (ICUs) is limited. These devices have notable problems, such as inefficient manufacturing and cumbersome hardware for medical staff and patients. In this study, we propose a simplified cardiopulmonary monitoring system and present a protocol for a single-center prospective study to evaluate the efficacy of the proposed system compared with those from the conventional monitoring system. The system was designed to continuously measure electrocardiogram, respiration rate, and oxygen saturation in a stand-alone device with an intuitive data visualization platform and automatic data collection. The accuracy of the data measured from the proposed device will be pre-validated by comparing them with those from the reference device. Medical staff from the St. Vincent’s Hospital ICU will complete a five-point Likert-type scale questionnaire regarding their experience with conventional ICU monitoring systems. The result will be compared with the second questionnaire conducted after deploying the system. Since this is a study proposal paper, we do not have any data on this study yet. However, compared with the conventional patient monitoring system, the proposed device should be a promising method to relieve medical staff fatigue and that of the patients who must wear and attach the monitoring device for a long time. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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14 pages, 7789 KiB  

Open AccessArticle

Expression Recognition of Multiple Faces Using a Convolution Neural Network Combining the Haar Cascade Classifier

by Jui-Feng Yeh, Kuei-Mei Lin, Chia-Chen Chang and Ting-Hao Wang

Appl. Sci. 2023, 13(23), 12737; https://doi.org/10.3390/app132312737 - 28 Nov 2023

Cited by 1 | Viewed by 1064

Abstract

Facial expression serves as the primary means for humans to convey emotions and communicate social signals. In recent years, facial expression recognition has become a viable application within medical systems because of the rapid development of artificial intelligence and computer vision. However, traditional [...] Read more.

Facial expression serves as the primary means for humans to convey emotions and communicate social signals. In recent years, facial expression recognition has become a viable application within medical systems because of the rapid development of artificial intelligence and computer vision. However, traditional facial expression recognition faces several challenges. The approach is designed to investigate the processing of facial expressions in real-time systems involving multiple individuals. These factors impact the accuracy and robustness of the model. In this paper, we adopted the Haar cascade classifier to extract facial features and utilized convolutional neural networks (CNNs) as the backbone model to achieve an efficient system. The proposed approach achieved an accuracy of approximately 70% on the FER-2013 dataset in the experiment. This result represents an improvement of 7.83% compared to that of the baseline system. This significant enhancement improves the accuracy of facial expression recognition. Herein, the proposed approach also extended to multiple face expression recognition; the module was further experimented with and obtained promising results. The outcomes of this research will establish a solid foundation for real-time monitoring and prevention of conditions such as depression through an emotion alert system. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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15 pages, 5272 KiB  

Open AccessArticle

Design, Fabrication, and Preliminary Validation of Patient-Specific Spine Section Phantoms for Use in Training Spine Surgeons Outside the Operating Room/Theatre

by Marina Carbone, Rosanna Maria Viglialoro, Sara Stagnari, Sara Condino, Marco Gesi, Michelangelo Scaglione and Paolo Domenico Parchi

Bioengineering 2023, 10(12), 1345; https://doi.org/10.3390/bioengineering10121345 - 23 Nov 2023

Viewed by 829

Abstract

Pedicle screw fixation (PSF) demands rigorous training to mitigate the risk of severe neurovascular complications arising from screw misplacement. This paper introduces a patient-specific phantom designed for PSF training, extending a portion of the learning process beyond the confines of the surgical room. [...] Read more.

Pedicle screw fixation (PSF) demands rigorous training to mitigate the risk of severe neurovascular complications arising from screw misplacement. This paper introduces a patient-specific phantom designed for PSF training, extending a portion of the learning process beyond the confines of the surgical room. Six phantoms of the thoracolumbar region were fabricated from radiological datasets, combining 3D printing and casting techniques. The phantoms were employed in three training sessions by a fifth-year resident who performed full training on all six phantoms; he/she placed a total of 57 pedicle screws. Analysis of the learning curve, focusing on time per screw and positioning accuracy, revealed attainment of an asymptotic performance level (around 3 min per screw) after 40 screws. The phantom’s efficacy was evaluated by three experts and six residents, each inserting a minimum of four screws. Initial assessments confirmed face, content, and construct validity, affirming the patient-specific phantoms as a valuable training resource. These proposed phantoms exhibit great promise as an essential tool in surgical training as they exhibited a demonstrable learning effect on the PSF technique. This study lays the foundation for further exploration and underscores the potential impact of these patient-specific phantoms on the future of spinal surgical education. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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19 pages, 3300 KiB  

Open AccessArticle

In Vivo Assessment of Skin Surface Pattern: Exploring Its Potential as an Indicator of Bone Biomechanical Properties

by Jean-Charles Aurégan, Catherine Bosser, Manon Bachy-Razzouk, Morad Bensidhoum and Thierry Hoc

Bioengineering 2023, 10(12), 1338; https://doi.org/10.3390/bioengineering10121338 - 21 Nov 2023

Viewed by 1025

Abstract

The mechanical properties of bone tissue are the result of a complex process involving collagen–crystal interactions. The mineral density of the bone tissue is correlated with bone strength, whereas the characteristics of collagen are often associated with the ductility and toughness of the [...] Read more.

The mechanical properties of bone tissue are the result of a complex process involving collagen–crystal interactions. The mineral density of the bone tissue is correlated with bone strength, whereas the characteristics of collagen are often associated with the ductility and toughness of the bone. From a clinical perspective, bone mineral density alone does not satisfactorily explain skeletal fragility. However, reliable in vivo markers of collagen quality that can be easily used in clinical practice are not available. Hence, the objective of the present study is to examine the relationship between skin surface morphology and changes in the mechanical properties of the bone. An experimental study was conducted on healthy children (n = 11), children with osteogenesis imperfecta (n = 13), and women over 60 years of age (n = 22). For each patient, the skin characteristic length (SCL) of the forearm skin surface was measured. The SCL quantifies the geometric patterns formed by wrinkles on the skin’s surface, both in terms of size and elongation. The greater the SCL, the more deficient was the organic collagen matrix. In addition, the bone volume fraction and mechanical properties of the explanted femoral head were determined for the elderly female group. The mean SCL values of the healthy children group were significantly lower than those of the elderly women and osteogenesis imperfecta groups. For the aged women group, no significant differences were indicated in the elastic mechanical parameters, whereas bone toughness and ductility decreased significantly as the SCL increased. In conclusion, in bone collagen pathology or bone aging, the SCL is significantly impaired. This in vivo skin surface parameter can be a non-invasive tool to improve the estimation of bone matrix quality and to identify subjects at high risk of bone fracture. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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18 pages, 2029 KiB  

Open AccessArticle

The Relationship between Health Insurance and Pharmaceutical Innovation: An Empirical Study Based on Meta-Analysis

by Chenchen Fan, Xiaoting Song and Chunyan Li

Healthcare 2023, 11(22), 2916; https://doi.org/10.3390/healthcare11222916 - 07 Nov 2023

Viewed by 1108

Abstract

The growing research interest in the relationship between health insurance and pharmaceutical innovation is driven by their significant impact on healthcare optimization and pharmaceutical development. The existing literature, however, lacks consensus on this relationship and provides no evidence of the magnitude of a [...] Read more.

The growing research interest in the relationship between health insurance and pharmaceutical innovation is driven by their significant impact on healthcare optimization and pharmaceutical development. The existing literature, however, lacks consensus on this relationship and provides no evidence of the magnitude of a correlation. In this context, this study employs meta-analysis to explore the extent to which health insurance affects pharmaceutical innovation. It analyzes 202 observations from 14 independent research samples, using the regression coefficient of health insurance on pharmaceutical innovation as the effect size. The results reveal that there is a strong positive correlation between health insurance and pharmaceutical innovation (r = 0.367, 95% CI = [0.294, 0.436]). Public health insurance exhibits a stronger promoting effect on pharmaceutical innovation than commercial health insurance. The relationship between health insurance and pharmaceutical innovation is moderated by the country of sample origin, data range, journal type, journal impact factor, type of health insurance, and research perspective. Our research findings further elucidate the relationship mechanism between health insurance and pharmaceutical innovation, providing a valuable reference for future explorations in pharmaceutical fields. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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12 pages, 2629 KiB  

Open AccessArticle

Creating an AI-Enhanced Morse Code Translation System Based on Images for People with Severe Disabilities

by Chung-Min Wu, Yeou-Jiunn Chen, Shih-Chung Chen and Sheng-Feng Zheng

Bioengineering 2023, 10(11), 1281; https://doi.org/10.3390/bioengineering10111281 - 03 Nov 2023

Viewed by 1260

Abstract

(1) Background: Patients with severe physical impairments (spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis) often have limited mobility due to physical limitations, and may even be bedridden all day long, losing the ability to take care of themselves. In more severe cases, [...] Read more.

(1) Background: Patients with severe physical impairments (spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis) often have limited mobility due to physical limitations, and may even be bedridden all day long, losing the ability to take care of themselves. In more severe cases, the ability to speak may even be lost, making even basic communication very difficult. (2) Methods: This research will design a set of image-assistive communication equipment based on artificial intelligence to solve communication problems of daily needs. Using artificial intelligence for facial positioning, and facial-motion-recognition-generated Morse code, and then translating it into readable characters or commands, it allows users to control computer software by themselves and communicate through wireless networks or a Bluetooth protocol to control environment peripherals. (3) Results: In this study, 23 human-typed data sets were subjected to recognition using fuzzy algorithms. The average recognition rates for expert-generated data and data input by individuals with disabilities were 99.83% and 98.6%, respectively. (4) Conclusions: Through this system, users can express their thoughts and needs through their facial movements, thereby improving their quality of life and having an independent living space. Moreover, the system can be used without touching external switches, greatly improving convenience and safety. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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15 pages, 3612 KiB  

Open AccessArticle

Heart Murmur Classification Using a Capsule Neural Network

by Yu-Ting Tsai, Yu-Hsuan Liu, Zi-Wei Zheng, Chih-Cheng Chen and Ming-Chih Lin

Bioengineering 2023, 10(11), 1237; https://doi.org/10.3390/bioengineering10111237 - 24 Oct 2023

Viewed by 1894

Abstract

The healthcare industry has made significant progress in the diagnosis of heart conditions due to the use of intelligent detection systems such as electrocardiograms, cardiac ultrasounds, and abnormal sound diagnostics that use artificial intelligence (AI) technology, such as convolutional neural networks (CNNs). Over [...] Read more.

The healthcare industry has made significant progress in the diagnosis of heart conditions due to the use of intelligent detection systems such as electrocardiograms, cardiac ultrasounds, and abnormal sound diagnostics that use artificial intelligence (AI) technology, such as convolutional neural networks (CNNs). Over the past few decades, methods for automated segmentation and classification of heart sounds have been widely studied. In many cases, both experimental and clinical data require electrocardiography (ECG)-labeled phonocardiograms (PCGs) or several feature extraction techniques from the mel-scale frequency cepstral coefficient (MFCC) spectrum of heart sounds to achieve better identification results with AI methods. Without good feature extraction techniques, the CNN may face challenges in classifying the MFCC spectrum of heart sounds. To overcome these limitations, we propose a capsule neural network (CapsNet), which can utilize iterative dynamic routing methods to obtain good combinations for layers in the translational equivariance of MFCC spectrum features, thereby improving the prediction accuracy of heart murmur classification. The 2016 PhysioNet heart sound database was used for training and validating the prediction performance of CapsNet and other CNNs. Then, we collected our own dataset of clinical auscultation scenarios for fine-tuning hyperparameters and testing results. CapsNet demonstrated its feasibility by achieving validation accuracies of 90.29% and 91.67% on the test dataset. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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11 pages, 1494 KiB  

Open AccessArticle

5G-Based Smart Healthcare and Mobile Network Security: Combating Fake Base Stations

by I-Hsien Liu, Meng-Huan Lee, Hsiao-Ching Huang and Jung-Shian Li

Appl. Sci. 2023, 13(20), 11565; https://doi.org/10.3390/app132011565 - 23 Oct 2023

Viewed by 997

Abstract

New mobile network technologies, particularly 5G, have spurred a growth in smart healthcare networks. They enable real-time monitoring, personalized treatments, and more. However, these transformative capabilities have also uncovered potential vulnerabilities, emphasizing the urgency to safeguard patient data and healthcare services. This study [...] Read more.

New mobile network technologies, particularly 5G, have spurred a growth in smart healthcare networks. They enable real-time monitoring, personalized treatments, and more. However, these transformative capabilities have also uncovered potential vulnerabilities, emphasizing the urgency to safeguard patient data and healthcare services. This study analyzes the existing research on 5G-based smart healthcare network security with a specific emphasis on fake base station attacks. The research investigates potential security measures to mitigate the impact of fake base station attacks. And based on those findings, we propose a detection scheme to help combat fake base station threats effectively and to avoid the need to install individual apps on smart devices, providing a foothold for future efforts to develop and deploy better countermeasures. To ensure a secure and resilient ecosystem for 5G-based smart healthcare, continuous research and proactive measures are required. By staying vigilant and committed to research and development, we can protect patient privacy, ensure secure data transmission, and enhance the quality of services within smart healthcare networks and other mobile network applications alike. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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12 pages, 1846 KiB  

Open AccessArticle

UY-NET: A Two-Stage Network to Improve the Result of Detection in Colonoscopy Images

by Cheng-Si He, Chen-Ji Wang, Jhong-Wei Wang and Yuan-Chen Liu

Appl. Sci. 2023, 13(19), 10800; https://doi.org/10.3390/app131910800 - 28 Sep 2023

Cited by 1 | Viewed by 830

Abstract

The human digestive system is susceptible to various viruses and bacteria, which can lead to the development of lesions, disorders, and even cancer. According to statistics, colorectal cancer has been a leading cause of death in Taiwan for years. To reduce its mortality [...] Read more.

The human digestive system is susceptible to various viruses and bacteria, which can lead to the development of lesions, disorders, and even cancer. According to statistics, colorectal cancer has been a leading cause of death in Taiwan for years. To reduce its mortality rate, clinicians must detect and remove polyps during gastrointestinal (GI) tract examinations. Recently, colonoscopies have been conducted to examine patients’ colons. Even so, polyps sometimes remain undetected. To help medical professionals better identify abnormalities, advanced deep learning algorithms that can accurately detect colorectal polyps from images should be developed. Prompted by this proposition, the present study combined U-Net and YOLOv4 to create a two-stage network algorithm called UY-Net. This new algorithm was tested using colonoscopy images from the Kvasir-SEG dataset. Results showed that UY-Net was significantly accurate in detecting polyps. It also outperformed YOLOv4, YOLOv3-spp, Faster R-CNN, and RetinaNet by achieving higher spatial accuracy and overall accuracy of object detection. As the empirical evidence suggests, two-stage network algorithms like UY-Net will be a reliable and promising aid to image detection in healthcare. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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13 pages, 1993 KiB  

Open AccessArticle

Influence of Induced Environment Oscillations on Limits of Stability in Healthy Adults

by Piotr Wodarski, Grzegorz Gruszka, Marta Chmura, Marek Ples, Marek Gzik and Jacek Jurkojć

Appl. Sci. 2023, 13(18), 10331; https://doi.org/10.3390/app131810331 - 15 Sep 2023

Viewed by 513

Abstract

(1) Background: Human balance and equilibrium-maintaining abilities have been widely researched up to this day. Numerous publications have investigated the possibilities of enhancing these abilities, bringing the patient back to their original capabilities post-disease or accident, and training for fall prevention. Virtual reality [...] Read more.

(1) Background: Human balance and equilibrium-maintaining abilities have been widely researched up to this day. Numerous publications have investigated the possibilities of enhancing these abilities, bringing the patient back to their original capabilities post-disease or accident, and training for fall prevention. Virtual reality technology (VR) is becoming a progressively more renowned technique for performing or enhancing rehabilitation or training. We aimed to explore whether the introduction of scenery oscillation can influence a person’s limits of stability. (2) Methods: Sixteen healthy adults participated in measurements. Each of them underwent 10 trials, during which subjects were supposed to, on acoustic cue, lean as far forward and back as possible, without raising their heels or toes. Two trials were conducted without the use of VR, four with oscillating scenery, one with stationary scenery, one with displayed darkness, and two trials were performed for reference, which did not require leaning nor used VR technology. (3) Results: For the total as well as for each foot separately, COP displacements and velocities were calculated and analyzed. A post-hoc Wilcoxon pairwise test with Holm’s correction was performed, resulting in 420 returned p-values, 4 of which indicated significant differences between medians when comparing trials with 0.2 Hz oscillating scenery with trials with eyes open and closed. (4) Conclusions: No statistically significant differences at α = 0.05 between reached maximums in trials using VR and trials without it were found, only trials using 0.2 Hz oscillations displayed statistically significant differences when comparing velocities of leaning. The authors believe that such oscillations resemble naturally occurring tinnitus; additionally, low-frequency oscillations are believed to influence postural balance more than high-frequency ones, therefore affecting the velocity and displacements of COP the most. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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15 pages, 235 KiB  

Open AccessArticle

Exploring Internal Conflicts and Collaboration of a Hospital Home Healthcare Team: A Grounded Theory Approach

by Pei-Chun Tai and Shofang Chang

Healthcare 2023, 11(18), 2478; https://doi.org/10.3390/healthcare11182478 - 07 Sep 2023

Cited by 2 | Viewed by 845

Abstract

An aging society is on the rise, leading to a variety of caregiving issues. The Taiwanese government has been implementing a home healthcare integration plan since 2015, aimed at integrating and forming interdisciplinary care teams with medical institutions. This study explores the internal [...] Read more.

An aging society is on the rise, leading to a variety of caregiving issues. The Taiwanese government has been implementing a home healthcare integration plan since 2015, aimed at integrating and forming interdisciplinary care teams with medical institutions. This study explores the internal conflict factors among hospital home healthcare team members at a district teaching hospital in Taichung, Taiwan, and it seeks a better collaboration model between them. Semi-structured in-depth interviews were conducted with seven hospital home healthcare team members. Data analysis was based on grounded theory, with research quality relying on the triangulation and consistency analysis methods. The results show that “work overload”, “resource overuse”, “inconsistent assessment”, “limited resources”, “communication cost”, and “lack of incentives” are the major conflicts among the team. This study proposed the following collaboration model, including “identifying the internal stakeholders of a home healthcare team” and “the key stakeholders as referral coordinators”, “patient-centered resource allocation”, and “teamwork orientation”. The study recommends that within a teamwork-oriented home healthcare team, its members should proactively demonstrate their role responsibilities and actively provide support to one another. Only through patient-centered resource allocation and mutual respect can the goal of seamless home healthcare be achieved. The content of the research and samples were approved by the hospital ethics committee (REC108-18). Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

16 pages, 7515 KiB  

Open AccessArticle

A Novel Elastic Sensor Sheet for Pressure Injury Monitoring: Design, Integration, and Performance Analysis

by Mohammad Mohammad Amini, Mohammad Ghassem Farajzadeh Devin, Paulo Alves, Davood Fanaei Sheikholeslami, Fatemeh Hariri, Rogério Dionísio, Mahdi Faghihi, Fernando Reinaldo, José Carlos Metrôlho and Luis Fonseca

Electronics 2023, 12(17), 3655; https://doi.org/10.3390/electronics12173655 - 30 Aug 2023

Viewed by 896

Abstract

This study presents the SENSOMATT sensor sheet, a novel, non-invasive pressure monitoring technology intended for placement beneath a mattress. The development and design process of the sheet, which includes a novel sensor arrangement, material selection, and incorporation of an elastic rubber sheet, is [...] Read more.

This study presents the SENSOMATT sensor sheet, a novel, non-invasive pressure monitoring technology intended for placement beneath a mattress. The development and design process of the sheet, which includes a novel sensor arrangement, material selection, and incorporation of an elastic rubber sheet, is investigated in depth. Highlighted features include the ability to adjust to varied mattress sizes and the incorporation of AI technology for pressure mapping. A comparison with conventional piezoelectric contact sensor sheets demonstrates the better accuracy of the SENSOMATT sensor for monitoring pressures beneath a mattress. The report highlights the sensor network’s cost-effectiveness, durability, and enhanced data measurement, alongside the problems experienced in its design. Evaluations of performance under diverse settings contribute to a full understanding of its potential pressure injury prediction and patient care applications. Proposed future paths for the SENSOMATT sensor sheet include clinical validation, more cost and performance improvement, wireless connection possibilities, and improved long-term monitoring data analysis. The study concludes that the SENSOMATT sensor sheet has the potential to transform pressure injury prevention techniques in healthcare. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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13 pages, 2254 KiB  

Open AccessArticle

Convolutional Neural Network and Language Model-Based Sequential CT Image Captioning for Intracerebral Hemorrhage

by Gi-Youn Kim, Byoung-Doo Oh, Chulho Kim and Yu-Seop Kim

Appl. Sci. 2023, 13(17), 9665; https://doi.org/10.3390/app13179665 - 26 Aug 2023

Cited by 1 | Viewed by 835

Abstract

Intracerebral hemorrhage is a severe problem where more than one-third of patients die within a month. In diagnosing intracranial hemorrhage, neuroimaging examinations are essential. As a result, the interpretation of neuroimaging becomes a crucial process in medical procedures. However, human-based image interpretation has [...] Read more.

Intracerebral hemorrhage is a severe problem where more than one-third of patients die within a month. In diagnosing intracranial hemorrhage, neuroimaging examinations are essential. As a result, the interpretation of neuroimaging becomes a crucial process in medical procedures. However, human-based image interpretation has inherent limitations, as it can only handle a restricted range of tasks. To address this, a study on medical image captioning has been conducted, but it primarily focused on single medical images. However, actual medical images often consist of continuous sequences, such as CT scans, making it challenging to directly apply existing studies. Therefore, this paper proposes a CT image captioning model that utilizes a 3D-CNN model and distilGPT-2. In this study, four combinations of 3D-CNN models and language models were compared and analyzed for their performance. Additionally, the impact of applying penalties to the loss function and adjusting penalty values during the training process was examined. The proposed CT image captioning model demonstrated a maximum BLEU score of 0.35 on the in-house dataset, and it was observed that the text generated by the model became more similar to human interpretations in medical image reports with the application of loss function penalties. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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14 pages, 1701 KiB  

Open AccessArticle

Improved Lightweight Convolutional Neural Network for Finger Vein Recognition System

by Chih-Hsien Hsia, Liang-Ying Ke and Sheng-Tao Chen

Bioengineering 2023, 10(8), 919; https://doi.org/10.3390/bioengineering10080919 - 03 Aug 2023

Cited by 2 | Viewed by 1280

Abstract

Computer vision (CV) technology and convolutional neural networks (CNNs) demonstrate superior feature extraction capabilities in the field of bioengineering. However, during the capturing process of finger-vein images, translation can cause a decline in the accuracy rate of the model, making it challenging to [...] Read more.

Computer vision (CV) technology and convolutional neural networks (CNNs) demonstrate superior feature extraction capabilities in the field of bioengineering. However, during the capturing process of finger-vein images, translation can cause a decline in the accuracy rate of the model, making it challenging to apply CNNs to real-time and highly accurate finger-vein recognition in various real-world environments. Moreover, despite CNNs’ high accuracy, CNNs require many parameters, and existing research has confirmed their lack of shift-invariant features. Based on these considerations, this study introduces an improved lightweight convolutional neural network (ILCNN) for finger vein recognition. The proposed model incorporates a diverse branch block (DBB), adaptive polyphase sampling (APS), and coordinate attention mechanism (CoAM) with the aim of improving the model’s performance in accurately identifying finger vein features. To evaluate the effectiveness of the model in finger vein recognition, we employed the finger-vein by university sains malaysia (FV-USM) and PLUSVein dorsal-palmar finger-vein (PLUSVein-FV3) public database for analysis and comparative evaluation with recent research methodologies. The experimental results indicate that the finger vein recognition model proposed in this study achieves an impressive recognition accuracy rate of 99.82% and 95.90% on the FV-USM and PLUSVein-FV3 public databases, respectively, while utilizing just 1.23 million parameters. Moreover, compared to the finger vein recognition approaches proposed in previous studies, the ILCNN introduced in this work demonstrated superior performance. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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12 pages, 7177 KiB  

Open AccessArticle

An Experimental Evaluation of Respiration by Monitoring Ribcage Motion

by Marco Ceccarelli, Manuel D’Onofrio, Vincenzo Ambrogi and Matteo Russo

Appl. Sci. 2023, 13(15), 8938; https://doi.org/10.3390/app13158938 - 03 Aug 2023

Viewed by 777

Abstract

This paper aims to make an early diagnosis of respiratory disorders by measuring ribcage motion. A statistically significant numerical evaluation of the biomechanics of respiration is obtained through the acquisition of the kinematics of the sixth rib. We report the results of an [...] Read more.

This paper aims to make an early diagnosis of respiratory disorders by measuring ribcage motion. A statistically significant numerical evaluation of the biomechanics of respiration is obtained through the acquisition of the kinematics of the sixth rib. We report the results of an experimental campaign that has been conducted using a RESPIRholter prototype for efficient and comfortable respiration monitoring on two groups of volunteers, one with healthy people and the other with chest-operated patients. The data from repeated acquisitions are statistically processed to analyze results in terms of angular motion and linear acceleration, which can be used to characterize and classify respiration motion. This experimental campaign can be considered a first result for the construction of a database useful for a reference of diagnostics, as reported by the discussed example case study. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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16 pages, 3793 KiB  

Open AccessArticle

An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring

by Yusra M. Obeidat and Ali M. Alqudah

Appl. Sci. 2023, 13(14), 8273; https://doi.org/10.3390/app13148273 - 17 Jul 2023

Cited by 6 | Viewed by 2488

Abstract

In recent years, there has been a growing demand for affordable and user-friendly medical diagnostic devices due to the rise in global diseases. This study focuses on the development of an embedded system based on Raspberry Pi that enables faster and more efficient [...] Read more.

In recent years, there has been a growing demand for affordable and user-friendly medical diagnostic devices due to the rise in global diseases. This study focuses on the development of an embedded system based on Raspberry Pi that enables faster and more efficient monitoring of electrocardiogram (ECG). The incorporation of Raspberry Pi allows for both wireless and wired interfaces, facilitating the creation of an ECG diagnostic embedded system capable of real-time detection and immediate response to any abnormalities in heart functionality. The system presented in this research encompasses a comprehensive electronic circuit comprising analog and digital components to measure and display the ECG signal. Within the analog section, the circuit performs essential signal conditioning tasks, such as signal amplification and noise filtering, ensuring a clean signal within the desired frequency range. The entire system is powered using a power bank. The digital segment incorporates an analog-to-digital converter necessary for converting the received analog signal into a digital format compatible with Raspberry Pi. A graphical liquid-crystal display is utilized to display the measured signal. The device successfully measures ECG signals at various heart rates, capturing all crucial peaks that can be used as indicators of an individual’s health condition. By comparing the signals obtained from healthy individuals with those exhibiting heart arrhythmias, valuable insights can be gained regarding their health status. The proposed system aims to be portable, cost-effective, and user-friendly in different environments. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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15 pages, 332 KiB  

Open AccessArticle

Factors Influencing the Effectiveness of E-Learning in Healthcare: A Fuzzy ANP Study

by Seyed Faraz Mahdavi Ardestani, Sasan Adibi, Arman Golshan and Paria Sadeghian

Healthcare 2023, 11(14), 2035; https://doi.org/10.3390/healthcare11142035 - 16 Jul 2023

Cited by 1 | Viewed by 2364

Abstract

E-learning has transformed the healthcare education system by providing healthcare professionals with training and development opportunities, regardless of their location. However, healthcare professionals in remote or rural areas face challenges such as limited access to educational resources, lack of reliable internet connectivity, geographical [...] Read more.

E-learning has transformed the healthcare education system by providing healthcare professionals with training and development opportunities, regardless of their location. However, healthcare professionals in remote or rural areas face challenges such as limited access to educational resources, lack of reliable internet connectivity, geographical isolation, and limited availability of specialized training programs and instructors. These challenges hinder their access to e-learning opportunities and impede their professional development. To address this issue, a study was conducted to identify the factors that influence the effectiveness of e-learning in healthcare. A literature review was conducted, and two questionnaires were distributed to e-learning experts to assess primary variables and identify the most significant factor. The Fuzzy Analytic Network Process (Fuzzy ANP) was used to identify the importance of selected factors. The study found that success, satisfaction, availability, effectiveness, readability, and engagement are the main components ranked in order of importance. Success was identified as the most significant factor. The study results highlight the benefits of e-learning in healthcare, including increased accessibility, interactivity, flexibility, knowledge management, and cost efficiency. E-learning offers a solution to the challenges of professional development faced by healthcare professionals in remote or rural areas. The study provides insights into the factors that influence the effectiveness of e-learning in healthcare and can guide the development of future e-learning programs. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

17 pages, 5513 KiB  

Open AccessArticle

An Electronic Microsaccade Circuit with Charge-Balanced Stimulation and Flicker Vision Prevention for an Artificial Eyeball System

by Yaogan Liang, Kohei Nakamura, Bang Du, Shengwei Wang, Bunta Inoue, Yuta Aruga, Hisashi Kino, Takafumi Fukushima, Koji Kiyoyama and Tetsu Tanaka

Electronics 2023, 12(13), 2836; https://doi.org/10.3390/electronics12132836 - 27 Jun 2023

Viewed by 975

Abstract

This paper presents the first circuit that enables microsaccade function in an artificial eyeball system. Currently, the artificial eyeball is receiving increasing development in vision restoration. The main challenge is that the human eye is born with microsaccade that helps refresh vision, avoiding [...] Read more.

This paper presents the first circuit that enables microsaccade function in an artificial eyeball system. Currently, the artificial eyeball is receiving increasing development in vision restoration. The main challenge is that the human eye is born with microsaccade that helps refresh vision, avoiding perception fading while the gaze is fixed for a long period, and without microsaccade, high-quality vision restoration is difficult. The proposed electronic microsaccade (E-μSaccade) circuit addresses the issue, and it is intrinsically safe because only charge-balanced stimulus pulses are allowed for stimulation. The E-μSaccade circuit adopts light-to-frequency modulation; due to the circuit’s leakage and dark current of light-sensitive elements, stimulus pulses of a frequency lower than tens of Hz occur, which is the cause of flickering vision. A flicker vision prevention (FVP) circuit is proposed to mitigate the issue. The proposed circuits are designed in a 0.18 μm standard CMOS process. The simulation and measurement results show that the E-μSaccade circuit helps refresh the stimulation pattern and blocks the low-frequency output. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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12 pages, 2309 KiB  

Open AccessCommunication

Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks

by Chien-Ching Chiu, Wei Chien, Kai-Xu Yu, Po-Hsiang Chen and Eng Hock Lim

Appl. Sci. 2023, 13(11), 6794; https://doi.org/10.3390/app13116794 - 02 Jun 2023

Cited by 1 | Viewed by 892

Abstract

In the past, many conventional algorithms, such as self-adaptive dynamic differential evolution and asynchronous particle swarm optimization, were used to reconstruct buried objects in the frequency domain; these were unfortunately time-consuming during the iterative, repeated computing process of the scattered field. Consequently, we [...] Read more.

In the past, many conventional algorithms, such as self-adaptive dynamic differential evolution and asynchronous particle swarm optimization, were used to reconstruct buried objects in the frequency domain; these were unfortunately time-consuming during the iterative, repeated computing process of the scattered field. Consequently, we propose an innovative deep convolutional neural network approach to solve the electromagnetic inverse scattering problem for buried conductors in this paper. Different shapes of conductors are buried in one half-space and the electromagnetic wave from the other half-space is incident. The shape of the conductor can be reconstructed promptly by inputting the received scattered fields measured from the upper half-space into the deep convolutional neural network module, which avoids the computational complexity of Green’s function for training. Numerical results show that the root mean square error for differently shaped—circular, elliptical, arrow, peanut, four-petal, and three-petal—reconstructed images are, respectively, 2.95%, 3.11%, 17.81%, 15.10%, 14.14%, and 15.24%. Briefly speaking, not only can circular and elliptical buried conductors be reconstructed; some irregular shapes can be reconstructed well. On the contrary, the reconstruction result by U-Net for buried objects is worse since it is not able to obtain a good preliminary image by processing only the upper scattered field—that is, rather than the full space. In other words, our proposed deep convolutional neural network can efficiently solve the electromagnetic inverse scattering problem of buried conductors and provide a novel method for the microwave imaging of the buried conductors. This is the first successful attempt at using deep convolutional neural networks for buried conductors in the frequency domain, which may be useful for practical applications in various fields such as the medical, military, or industrial fields, including magnetic resonance imaging, mine detection and clearance, non-destructive testing, gas or wire pipeline detection, etc. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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13 pages, 688 KiB  

Open AccessArticle

Microwave Imaging for Half-Space Conductors Using the Whale Optimization Algorithm and the Spotted Hyena Optimizer

by Chien-Ching Chiu, Po-Hsiang Chen, Wei Chien, Eng Hock Lim and Guo-Zheng Chen

Appl. Sci. 2023, 13(10), 5857; https://doi.org/10.3390/app13105857 - 09 May 2023

Viewed by 924

Abstract

This research implements the whale optimization algorithm (WOA) and spotted hyena optimizer (SHO) in inverse scattering to regenerate the conductor shape concealed in the half-space. TM waves are irradiated from the other half-space to a perfect conductor with an unknown shape buried in [...] Read more.

This research implements the whale optimization algorithm (WOA) and spotted hyena optimizer (SHO) in inverse scattering to regenerate the conductor shape concealed in the half-space. TM waves are irradiated from the other half-space to a perfect conductor with an unknown shape buried in one half-space. The scattered field measured outside the conductor surface with the boundary condition is used to reconstruct the object using the WOA and SHO algorithms. Several scenarios of reconstruction accuracy were compared for the WOA and SHO. The numerical simulations prove that the WOA has a better reconstruction capability. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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16 pages, 7799 KiB  

Open AccessArticle

Leakage Current Detector and Warning System Integrated with Electric Meter

by Tsung-Hui Cheng, Chien-Hao Chen, Chien-Hung Lin, Bor-Horng Sheu, Chia-Hung Lin and Wen-Ping Chen

Electronics 2023, 12(9), 2123; https://doi.org/10.3390/electronics12092123 - 06 May 2023

Viewed by 4436

Abstract

Electrical power is essential in human life. Thus, the security and reliability of its supply are of critical importance in a country’s industrial and economic development. The leakage and improper use of electricity may cause serious problems such as fire and electrocution. To [...] Read more.

Electrical power is essential in human life. Thus, the security and reliability of its supply are of critical importance in a country’s industrial and economic development. The leakage and improper use of electricity may cause serious problems such as fire and electrocution. To prevent such incidents and minimize the loss of life and property, a leakage current detector and warning system are developed in this study. With a high-precision current transformer and high-gain linear converter, the detector effectively detects leakage current over 1 mA, which is validated in different methods. The detector can be integrated into widely used electric meters (Taipower Datong’s sub-meter model D4S) easily, and information on detected leakage current is transmitted to the cloud server through narrowband IoT wireless communication (NB-IoT) to warn users and management personnel of the electrical power line. The proposed detector and system are expected to prevent the fire caused by leakage current which was the main cause of the fire at homes and buildings and can be an effective means to manage the electrical powerline system and metering facilities. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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15 pages, 4460 KiB  

Open AccessArticle

Using Contactless Facial Image Recognition Technology to Detect Blood Oxygen Saturation

by Jui-Chuan Cheng, Tzung-Shiarn Pan, Wei-Cheng Hsiao, Wei-Hong Lin, Yan-Liang Liu, Te-Jen Su and Shih-Ming Wang

Bioengineering 2023, 10(5), 524; https://doi.org/10.3390/bioengineering10050524 - 26 Apr 2023

Cited by 2 | Viewed by 2250

Abstract

Since the outbreak of COVID-19, as of January 2023, there have been over 670 million cases and more than 6.8 million deaths worldwide. Infections can cause inflammation in the lungs and decrease blood oxygen levels, which can lead to breathing difficulties and endanger [...] Read more.

Since the outbreak of COVID-19, as of January 2023, there have been over 670 million cases and more than 6.8 million deaths worldwide. Infections can cause inflammation in the lungs and decrease blood oxygen levels, which can lead to breathing difficulties and endanger life. As the situation continues to escalate, non-contact machines are used to assist patients at home to monitor their blood oxygen levels without encountering others. This paper uses a general network camera to capture the forehead area of a person’s face, using the RPPG (remote photoplethysmography) principle. Then, image signal processing of red and blue light waves is carried out. By utilizing the principle of light reflection, the standard deviation and mean are calculated, and the blood oxygen saturation is computed. Finally, the effect of illuminance on the experimental values is discussed. The experimental results of this paper were compared with a blood oxygen meter certified by the Ministry of Health and Welfare in Taiwan, and the experimental results had only a maximum error of 2%, which is better than the 3% to 5% error rates in other studies The measurement time was only 30 s, which is better than the one minute reported using similar equipment in other studies. Therefore, this paper not only saves equipment expenses but also provides convenience and safety for those who need to monitor their blood oxygen levels at home. Future applications can combine the SpO2 detection software with camera-equipped devices such as smartphones and laptops. The public can detect SpO2 on their own mobile devices, providing a convenient and effective tool for personal health management. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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15 pages, 2842 KiB  

Open AccessArticle

Towards a Rapid-Turnaround Low-Depth Unbiased Metagenomics Sequencing Workflow on the Illumina Platforms

by Winston Lian Chye Koh, Si En Poh, Chun Kiat Lee, Tim Hon Man Chan, Gabriel Yan, Kiat Whye Kong, Lalita Lau, Wai Yip Thomas Lee, Clark Cheng, Shawn Hoon and Yiqi Seow

Bioengineering 2023, 10(5), 520; https://doi.org/10.3390/bioengineering10050520 - 25 Apr 2023

Cited by 1 | Viewed by 1655

Abstract

Unbiased metagenomic sequencing is conceptually well-suited for first-line diagnosis as all known and unknown infectious entities can be detected, but costs, turnaround time and human background reads in complex biofluids, such as plasma, hinder widespread deployment. Separate preparations of DNA and RNA also [...] Read more.

Unbiased metagenomic sequencing is conceptually well-suited for first-line diagnosis as all known and unknown infectious entities can be detected, but costs, turnaround time and human background reads in complex biofluids, such as plasma, hinder widespread deployment. Separate preparations of DNA and RNA also increases costs. In this study, we developed a rapid unbiased metagenomics next-generation sequencing (mNGS) workflow with a human background depletion method (HostEL) and a combined DNA/RNA library preparation kit (AmpRE) to address this issue. We enriched and detected bacterial and fungal standards spiked in plasma at physiological levels with low-depth sequencing (<1 million reads) for analytical validation. Clinical validation also showed 93% of plasma samples agreed with the clinical diagnostic test results when the diagnostic qPCR had a Ct < 33. The effect of different sequencing times was evaluated with the 19 h iSeq 100 paired end run, a more clinically palatable simulated iSeq 100 truncated run and the rapid 7 h MiniSeq platform. Our results demonstrate the ability to detect both DNA and RNA pathogens with low-depth sequencing and that iSeq 100 and MiniSeq platforms are compatible with unbiased low-depth metagenomics identification with the HostEL and AmpRE workflow. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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18 pages, 3408 KiB  

Open AccessArticle

A Non-Invasive Optical Multimodal Photoplethysmography-Near Infrared Spectroscopy Sensor for Measuring Intracranial Pressure and Cerebral Oxygenation in Traumatic Brain Injury

by Maria Roldan and Panicos A. Kyriacou

Appl. Sci. 2023, 13(8), 5211; https://doi.org/10.3390/app13085211 - 21 Apr 2023

Cited by 3 | Viewed by 1680

Abstract

(1) Background: Traumatic brain injuries (TBI) result in high fatality and lifelong disability rates. Two of the primary biomarkers in assessing TBI are intracranial pressure (ICP) and brain oxygenation. Both are assessed using standalone techniques, out of which ICP can only be assessed [...] Read more.

(1) Background: Traumatic brain injuries (TBI) result in high fatality and lifelong disability rates. Two of the primary biomarkers in assessing TBI are intracranial pressure (ICP) and brain oxygenation. Both are assessed using standalone techniques, out of which ICP can only be assessed utilizing invasive techniques. The motivation of this research is the development of a non-invasive optical multimodal monitoring technology for ICP and brain oxygenation which will enable the effective management of TBI patients. (2) Methods: a multiwavelength optical sensor was designed and manufactured so as to assess both parameters based on the pulsatile and non-pulsatile signals detected from cerebral backscatter light. The probe consists of four LEDs and three photodetectors that measure photoplethysmography (PPG) and near-infrared spectroscopy (NIRS) signals from cerebral tissue. (3) Results: The instrumentation system designed to acquire these optical signals is described in detail along with a rigorous technical evaluation of both the sensor and instrumentation. Bench testing demonstrated the right performance of the electronic circuits while a signal quality assessment showed good indices across all wavelengths, with the signals from the distal photodetector being of highest quality. The system performed well within specifications and recorded good-quality pulsations from a head phantom and provided non-pulsatile signals as expected. (4) Conclusions: This development paves the way for a multimodal non-invasive tool for the effective assessment of TBI patients. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2023)

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