Pub Date : 2024-04-12DOI: 10.3991/ijoe.v20i06.48001
Nguyen Thuy Van, Mohd Amran Mohd Daril, Masroor Ali, Muhammad Saleem Korejo
Psychological well-being is a cornerstone of student success in higher education. However, many students struggle with mental health challenges like stress, anxiety, and depression during and even after the Covid-19 pandemic. These challenges, often stemming from academic, personal, social, or career concerns, negatively impact student learning and development. This underscores the need for robust support systems within higher education (HE). Artificial intelligence (AI) emerges as a promising field in educational technology, offering students readily available guidance on their path to well-being. This research, guided by the PRISMA Statement 2015, provides an overview of AI applications in higher education through a systematic review. From an initial pool of 270 publications identified between year 2021 and 2023, finally, 24 articles met our inclusion criteria and were analyzed for the final synthesis. This paper revealed three key areas where AI-based systems can support student well-being: i) AI’s Advancement and Potential: Exploring the evolving capabilities and promise of AI in this context. ii) Building Effective AI Systems: Identifying crucial components for successful AI-based well-being interventions. iii) Barriers to Implementing AI in Higher Education: Addressing ethical considerations and challenges unique to academic settings. The conclusions and the road ahead from this research is the critical need for ethical, well-designed AI-based systems to overcome existing barriers and deliver exceptional student well-being support services. By prioritizing student mental health and providing them with the necessary tools and resources, we can empower them to achieve their full potential and thrive in their academic endeavors.
{"title":"Enhancing Psychological Well-being in Higher Education Post-Covid-19 Pandemic. The Role of AI-Based Support Systems—Bibliometric Reviews","authors":"Nguyen Thuy Van, Mohd Amran Mohd Daril, Masroor Ali, Muhammad Saleem Korejo","doi":"10.3991/ijoe.v20i06.48001","DOIUrl":"https://doi.org/10.3991/ijoe.v20i06.48001","url":null,"abstract":"Psychological well-being is a cornerstone of student success in higher education. However, many students struggle with mental health challenges like stress, anxiety, and depression during and even after the Covid-19 pandemic. These challenges, often stemming from academic, personal, social, or career concerns, negatively impact student learning and development. This underscores the need for robust support systems within higher education (HE). Artificial intelligence (AI) emerges as a promising field in educational technology, offering students readily available guidance on their path to well-being. This research, guided by the PRISMA Statement 2015, provides an overview of AI applications in higher education through a systematic review. From an initial pool of 270 publications identified between year 2021 and 2023, finally, 24 articles met our inclusion criteria and were analyzed for the final synthesis. This paper revealed three key areas where AI-based systems can support student well-being: i) AI’s Advancement and Potential: Exploring the evolving capabilities and promise of AI in this context. ii) Building Effective AI Systems: Identifying crucial components for successful AI-based well-being interventions. iii) Barriers to Implementing AI in Higher Education: Addressing ethical considerations and challenges unique to academic settings. The conclusions and the road ahead from this research is the critical need for ethical, well-designed AI-based systems to overcome existing barriers and deliver exceptional student well-being support services. By prioritizing student mental health and providing them with the necessary tools and resources, we can empower them to achieve their full potential and thrive in their academic endeavors.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140709849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.3991/ijoe.v20i06.48085
Mohd Amran Mohd Daril, Fozia Fatima, Samar Raza Talpur, Alhamzah F. Abbas
In the rapidly evolving landscape of healthcare, the digital transformation marked by healthcare 4.0 has spurred a surge in data generation, giving rise to ‘big data’. Big data analytics has become an effective tool in the healthcare industry, revolutionising medical research, patient care, and healthcare management. This study undertakes a meticulous bibliometric analysis, drawing upon a dataset of 2212 articles from the Scopus database spanning 2014 to 2023, to unravel the trajectory of big data analytics in healthcare. The research explores diverse dimensions, from the distribution of studies across years to the productivity rankings of journals, countries, and institutions, elucidating the evolving trends and key contributors. Co-authorship networks and keyword co-occurrence analysis reveal thematic clusters and intellectual structures, contributing to a nuanced understanding of the field. The results underscore the escalating global interest in the fusion of big data and healthcare, illuminating collaborations, and identifying influential players. Additionally, the study identifies pressing challenges, including security concerns and skill shortages, emphasizing the imperative of overcoming these barriers for effective big data applications in healthcare. Serving as a valuable resource for researchers, practitioners, and policymakers, this research not only captures the current landscape but also provides insights for future exploration, contributing to strategic planning in this dynamic domain.
{"title":"Unveiling the Landscape of Big Data Analytics in Healthcare: A Comprehensive Bibliometric Analysis","authors":"Mohd Amran Mohd Daril, Fozia Fatima, Samar Raza Talpur, Alhamzah F. Abbas","doi":"10.3991/ijoe.v20i06.48085","DOIUrl":"https://doi.org/10.3991/ijoe.v20i06.48085","url":null,"abstract":"In the rapidly evolving landscape of healthcare, the digital transformation marked by healthcare 4.0 has spurred a surge in data generation, giving rise to ‘big data’. Big data analytics has become an effective tool in the healthcare industry, revolutionising medical research, patient care, and healthcare management. This study undertakes a meticulous bibliometric analysis, drawing upon a dataset of 2212 articles from the Scopus database spanning 2014 to 2023, to unravel the trajectory of big data analytics in healthcare. The research explores diverse dimensions, from the distribution of studies across years to the productivity rankings of journals, countries, and institutions, elucidating the evolving trends and key contributors. Co-authorship networks and keyword co-occurrence analysis reveal thematic clusters and intellectual structures, contributing to a nuanced understanding of the field. The results underscore the escalating global interest in the fusion of big data and healthcare, illuminating collaborations, and identifying influential players. Additionally, the study identifies pressing challenges, including security concerns and skill shortages, emphasizing the imperative of overcoming these barriers for effective big data applications in healthcare. Serving as a valuable resource for researchers, practitioners, and policymakers, this research not only captures the current landscape but also provides insights for future exploration, contributing to strategic planning in this dynamic domain.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.3991/ijoe.v20i06.45773
A. Dahmouni, Abdelkaher Ait Abdelouahad, Yasser Aderghal, Ibrahim Guelzim, I. Bellamine, H. Silkan
Wireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature for the patients. It uses remote imaging techniques to explore various segments of the gastrointestinal (GI) tract, particularly the hard-to-reach small intestine, making it an effective alternative to traditional endoscopic techniques. However, physicians face a significant challenge when it comes to analyzing a large number of endoscopic images due to the effort and time required. It is therefore imperative to implement aided-diagnostic systems capable of automatically detecting suspicious areas for subsequent medical assessment. In this paper, we present a novel approach to identify gastrointestinal tract abnormalities from WCE images, with a particular focus on ulcerated areas. Our approach involves the use of the Median Robust Extended Local Binary Pattern (MRELBP) descriptor, which effectively overcomes the challenges faced when WCE image acquisition, such as variations in illumination and contrast, rotation, and noise. Using machine learning algorithms, we conducted experiments on the extensive Kvasir-Capsule dataset, and subsequently compared our results with recent relevant studies. Noteworthy is the fact that our approach achieved an accuracy of 97.04% with the SVM (RBF) classifier and 96.77% with the RF classifier.
{"title":"A Robust Approach for Ulcer Classification/Detection in WCE Images","authors":"A. Dahmouni, Abdelkaher Ait Abdelouahad, Yasser Aderghal, Ibrahim Guelzim, I. Bellamine, H. Silkan","doi":"10.3991/ijoe.v20i06.45773","DOIUrl":"https://doi.org/10.3991/ijoe.v20i06.45773","url":null,"abstract":"Wireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature for the patients. It uses remote imaging techniques to explore various segments of the gastrointestinal (GI) tract, particularly the hard-to-reach small intestine, making it an effective alternative to traditional endoscopic techniques. However, physicians face a significant challenge when it comes to analyzing a large number of endoscopic images due to the effort and time required. It is therefore imperative to implement aided-diagnostic systems capable of automatically detecting suspicious areas for subsequent medical assessment. In this paper, we present a novel approach to identify gastrointestinal tract abnormalities from WCE images, with a particular focus on ulcerated areas. Our approach involves the use of the Median Robust Extended Local Binary Pattern (MRELBP) descriptor, which effectively overcomes the challenges faced when WCE image acquisition, such as variations in illumination and contrast, rotation, and noise. Using machine learning algorithms, we conducted experiments on the extensive Kvasir-Capsule dataset, and subsequently compared our results with recent relevant studies. Noteworthy is the fact that our approach achieved an accuracy of 97.04% with the SVM (RBF) classifier and 96.77% with the RF classifier.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140708684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.3991/ijoe.v20i06.47881
Hedi Choura, Faten Chaabane, Mouna Baklouti, T. Frikha
Because of the availability of more than an actor and a wireless component in an e-health application, providing more security and safety to users of this type of applications is expected. Moreover, ensuring protection of data user available or shared within different services from any security attack becomes an important requirement. In this paper, we are interested essentially in the authentication process, and we propose an improved Landmarkbased algorithm as a tool to extract, firstly, key features from analysed faces, and hence to accelerate the authentication operation. The suggested approach beats other state-of-the-art works in terms of accuracy and speed-up attaining time execution constraint, according to experimental evaluations.
{"title":"An Optimized Effective Authentication Process for E-Health Application","authors":"Hedi Choura, Faten Chaabane, Mouna Baklouti, T. Frikha","doi":"10.3991/ijoe.v20i06.47881","DOIUrl":"https://doi.org/10.3991/ijoe.v20i06.47881","url":null,"abstract":"Because of the availability of more than an actor and a wireless component in an e-health application, providing more security and safety to users of this type of applications is expected. Moreover, ensuring protection of data user available or shared within different services from any security attack becomes an important requirement. In this paper, we are interested essentially in the authentication process, and we propose an improved Landmarkbased algorithm as a tool to extract, firstly, key features from analysed faces, and hence to accelerate the authentication operation. The suggested approach beats other state-of-the-art works in terms of accuracy and speed-up attaining time execution constraint, according to experimental evaluations.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140710807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.3991/ijoe.v20i06.46919
Shilpa Ankalaki, Vidyadevi G Biradar, Kishore Kumar Naik P, Geetabai S. Hukkeri
The timely detection of malnutrition in children is of paramount importance, as it allows for early intervention and treatment. This proactive approach not only prevents further health deterioration but also fosters proper growth, minimizing the long-term consequences of malnutrition, such as stunted growth, impaired cognitive development, and increased vulnerability to diseases. Our work encompasses the creation of a new dataset comprising images of children in Healthy, Undernourished, Stunting, and Wasting categories. The core objective is to assess the deep learning model performance in classifying these children images. The experimentation is carried out by varying epochs, batch size, optimizers AdamW, Adamax, and RMSprop; and different values of the learning rate 0.1, 0.01, 0.001, and 0.0001 during model training. The model is trained on image dataset constructed by cleaning images generated by the stable diffusion model. The model is tested on randomly selected child images from websites. The model successfully classified two classes with 95% accuracy, 97.6% F1 score, precision 97.6%, and 97.6% recall with Adam optimizers, 0.0001 learning rate, and Batch size 4. Additionally, for the four-class categorization scenario, the study broadens the classification. The model achieved 88.87% accuracy, 90.3% recall, 90.2% precision, and an F1 score of 90% for four-class categorization with AdamW optimization, 0.0001 learning rate, and batch size 6. These results are satisfactory for prediction of malnutrition category in children.
{"title":"A Deep Learning Approach for Malnutrition Detection","authors":"Shilpa Ankalaki, Vidyadevi G Biradar, Kishore Kumar Naik P, Geetabai S. Hukkeri","doi":"10.3991/ijoe.v20i06.46919","DOIUrl":"https://doi.org/10.3991/ijoe.v20i06.46919","url":null,"abstract":"The timely detection of malnutrition in children is of paramount importance, as it allows for early intervention and treatment. This proactive approach not only prevents further health deterioration but also fosters proper growth, minimizing the long-term consequences of malnutrition, such as stunted growth, impaired cognitive development, and increased vulnerability to diseases. Our work encompasses the creation of a new dataset comprising images of children in Healthy, Undernourished, Stunting, and Wasting categories. The core objective is to assess the deep learning model performance in classifying these children images. The experimentation is carried out by varying epochs, batch size, optimizers AdamW, Adamax, and RMSprop; and different values of the learning rate 0.1, 0.01, 0.001, and 0.0001 during model training. The model is trained on image dataset constructed by cleaning images generated by the stable diffusion model. The model is tested on randomly selected child images from websites. The model successfully classified two classes with 95% accuracy, 97.6% F1 score, precision 97.6%, and 97.6% recall with Adam optimizers, 0.0001 learning rate, and Batch size 4. Additionally, for the four-class categorization scenario, the study broadens the classification. The model achieved 88.87% accuracy, 90.3% recall, 90.2% precision, and an F1 score of 90% for four-class categorization with AdamW optimization, 0.0001 learning rate, and batch size 6. These results are satisfactory for prediction of malnutrition category in children.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140710334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.3991/ijoe.v20i06.46545
Amal Hallou, Tarik Fissaa, Hatim Hafiddi, Mahmoud Nassar
Integrating context awareness into the Internet of Things systems is essential for enhancing their adaptability to their context, particularly their user preferences and behaviors. This paper proposes an approach to model and develop context-aware self-adaptive IoT systems, capable of adapting their actions according to their users’ preferences. The approach consists of three main axes. The first axis involves establishing an overview of the system architecture that provides a high-level understanding of the various components of a context-aware IoT system. The second axis concerns the creation of a context-aware IoT systems meta-model, encapsulating the essential elements, relationships, and dependencies governing context awareness within the IoT system in a domain-independent manner. The third axis proposes a reinforcement learning reasoning process to enable intelligent decision-making within context- aware IoT systems. To validate the feasibility of the proposed approach, a simulation was conducted using the OpenAI Gym framework to emulate a context-aware smart home system. The results highlight the feasibility of the approach, and its potential to enhance real-life IoT systems’ awareness of their users’ context.
{"title":"Context-Aware IoT System Development Approach Based on Meta-Modeling and Reinforcement Learning","authors":"Amal Hallou, Tarik Fissaa, Hatim Hafiddi, Mahmoud Nassar","doi":"10.3991/ijoe.v20i06.46545","DOIUrl":"https://doi.org/10.3991/ijoe.v20i06.46545","url":null,"abstract":"Integrating context awareness into the Internet of Things systems is essential for enhancing their adaptability to their context, particularly their user preferences and behaviors. This paper proposes an approach to model and develop context-aware self-adaptive IoT systems, capable of adapting their actions according to their users’ preferences. The approach consists of three main axes. The first axis involves establishing an overview of the system architecture that provides a high-level understanding of the various components of a context-aware IoT system. The second axis concerns the creation of a context-aware IoT systems meta-model, encapsulating the essential elements, relationships, and dependencies governing context awareness within the IoT system in a domain-independent manner. The third axis proposes a reinforcement learning reasoning process to enable intelligent decision-making within context- aware IoT systems. To validate the feasibility of the proposed approach, a simulation was conducted using the OpenAI Gym framework to emulate a context-aware smart home system. The results highlight the feasibility of the approach, and its potential to enhance real-life IoT systems’ awareness of their users’ context.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.3991/ijoe.v20i06.45787
Sindhu K, A Nidhi Uday, Abhishek S J, Anjali S
Gait analysis is a valuable tool for making medical inferences and improving the diagnosis and treatment of mobility issues. This project aims to leverage gait analysis in addressing two important challenges: detecting knock knees and monitoring patients with Parkinson’s disease for falls. The project proposes the integration of gait analysis with yoga therapy to provide a unique and effective approach for correcting knock knees. A web user interface is developed to enable individuals to access the system, receive accurate feedback on their gait, and access yoga postures tailored to target knock knees. Additionally, a fall detection system is designed to monitor patients with Parkinson’s disease and notify caregivers or guardians in case of a fall. The implementation involves utilizing deep learning models, such as OpenPose model, a widely adopted deep learning framework for pose estimation and MediaPipe, another recognized framework used for building multimodal applied machine learning pipelines, to analyze gait patterns and detect knock knees and falls. The project aims to empower individuals in improving their gait, correcting knock knees, and enhancing their physical health, ultimately improving their quality of life and well-being.
{"title":"Gait Analysis—A Tool for Medical Inferences","authors":"Sindhu K, A Nidhi Uday, Abhishek S J, Anjali S","doi":"10.3991/ijoe.v20i06.45787","DOIUrl":"https://doi.org/10.3991/ijoe.v20i06.45787","url":null,"abstract":"Gait analysis is a valuable tool for making medical inferences and improving the diagnosis and treatment of mobility issues. This project aims to leverage gait analysis in addressing two important challenges: detecting knock knees and monitoring patients with Parkinson’s disease for falls. The project proposes the integration of gait analysis with yoga therapy to provide a unique and effective approach for correcting knock knees. A web user interface is developed to enable individuals to access the system, receive accurate feedback on their gait, and access yoga postures tailored to target knock knees. Additionally, a fall detection system is designed to monitor patients with Parkinson’s disease and notify caregivers or guardians in case of a fall. The implementation involves utilizing deep learning models, such as OpenPose model, a widely adopted deep learning framework for pose estimation and MediaPipe, another recognized framework used for building multimodal applied machine learning pipelines, to analyze gait patterns and detect knock knees and falls. The project aims to empower individuals in improving their gait, correcting knock knees, and enhancing their physical health, ultimately improving their quality of life and well-being.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Effective management of blood glucose levels in individuals with type 1 diabetes, especially after meals, is crucial for diabetes care. Artificial pancreas systems (APS) perform automated insulin delivery in subjects with type 1 diabetes mellitus (T1DM). In this study, an optimized fuzzy logic controller was designed to achieve a euglycemic range after a substantial meal intake. All in silico simulations were performed using the MATLAB/Simulink environment, leveraging control variability grid analysis (CVGA), and the performance of the controller was evaluated. The proposed controller is based on a fuzzy-logic control law designed in three stages. First, a nonlinear framework of the glucose-insulin regulatory system was identified based on the heavy meal protocol of three patients given as follows: for subject ID 117-1, a total of 295 gCHO; for subject ID 126-1, 236 gCHO; and subject ID 128-1, 394 gCHO over a day. Then, an iterative tree structure was employed to establish a stabilizing control rule for insulin delivery, integrating inputs from two Mamdani Fuzzy Inference System (FIS) objects. Finally, a genetic algorithm refines the control system by fine-tuning the uncertainty of the fuzzy membership functions. Two scenarios were considered for three patients to assess the performance of the proposed controller. The results indicated its effectiveness under various conditions, achieving a time in the range of 61.25%, 71% and 61.10% respectively for the three subjects. The obtained results are analyzed and compared with IMC and multi-objective output feedback controllers. The findings of the study reveal that the proposed controller shows promising advancements in tailored strategies for type 1 diabetes patients, outperforming the other controllers in terms of blood glucose regulation.
{"title":"Optimizing Blood Glucose Regulation in Type 1 Diabetes Patients via Genetic Algorithm-Based Fuzzy Logic Controller Considering Substantial Meal Protocol","authors":"Isah Ndakara Abubakar, Moad Essabbar, Hajar Saikouk","doi":"10.3991/ijoe.v20i06.46929","DOIUrl":"https://doi.org/10.3991/ijoe.v20i06.46929","url":null,"abstract":"Effective management of blood glucose levels in individuals with type 1 diabetes, especially after meals, is crucial for diabetes care. Artificial pancreas systems (APS) perform automated insulin delivery in subjects with type 1 diabetes mellitus (T1DM). In this study, an optimized fuzzy logic controller was designed to achieve a euglycemic range after a substantial meal intake. All in silico simulations were performed using the MATLAB/Simulink environment, leveraging control variability grid analysis (CVGA), and the performance of the controller was evaluated. The proposed controller is based on a fuzzy-logic control law designed in three stages. First, a nonlinear framework of the glucose-insulin regulatory system was identified based on the heavy meal protocol of three patients given as follows: for subject ID 117-1, a total of 295 gCHO; for subject ID 126-1, 236 gCHO; and subject ID 128-1, 394 gCHO over a day. Then, an iterative tree structure was employed to establish a stabilizing control rule for insulin delivery, integrating inputs from two Mamdani Fuzzy Inference System (FIS) objects. Finally, a genetic algorithm refines the control system by fine-tuning the uncertainty of the fuzzy membership functions. Two scenarios were considered for three patients to assess the performance of the proposed controller. The results indicated its effectiveness under various conditions, achieving a time in the range of 61.25%, 71% and 61.10% respectively for the three subjects. The obtained results are analyzed and compared with IMC and multi-objective output feedback controllers. The findings of the study reveal that the proposed controller shows promising advancements in tailored strategies for type 1 diabetes patients, outperforming the other controllers in terms of blood glucose regulation.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.3991/ijoe.v20i06.47293
Alaa A. Najim, S. Hamandi, Ahmed Alzubaidi
The Ilizarov fixator is a type of external fixator that is used to treat patients who have suffered injuries from accidents, bone shortening, or nonunion of the bone. The principle behind the Ilizarov fixator is that thin wires (called Kirschner wires) are used to support the bones and connect them to framed rings. Before being fastened to the rings, the wires are tensioned and drilled through the bones. This study suggests using a new parallel wires configuration at the same level on the same ring and two revised versions, which are divergent and convergent models, and compare them with standard wires, 60 angle wires model. All models were designed using SolidWorks, a computer-aided design (CAD) software, and then analyzed in four conditions (axial compression, medial bending, posterior bending, and torsion) with Finite Element Analysis (FEA) using Ansys Workbench 2020 R2. Mechanical testing was conducted to validate the FEA results, A simple model consisting of a single ring, two K-wires, and polylactic acid (PLA) cylinders was utilized in a tensile test. It has been concluded from the results that the parallel model and its improvement have higher stiffness to axial compression, medial bending, and torsion, but a lower posterior bending stiffness, except the divergent model with 8-hole separation which has a relatively acceptable stiffness for posterior bending.
{"title":"The Effect of Twisted Wire Configuration on the Stability of External Fixator: A Biomechanical Study","authors":"Alaa A. Najim, S. Hamandi, Ahmed Alzubaidi","doi":"10.3991/ijoe.v20i06.47293","DOIUrl":"https://doi.org/10.3991/ijoe.v20i06.47293","url":null,"abstract":"The Ilizarov fixator is a type of external fixator that is used to treat patients who have suffered injuries from accidents, bone shortening, or nonunion of the bone. The principle behind the Ilizarov fixator is that thin wires (called Kirschner wires) are used to support the bones and connect them to framed rings. Before being fastened to the rings, the wires are tensioned and drilled through the bones. This study suggests using a new parallel wires configuration at the same level on the same ring and two revised versions, which are divergent and convergent models, and compare them with standard wires, 60 angle wires model. All models were designed using SolidWorks, a computer-aided design (CAD) software, and then analyzed in four conditions (axial compression, medial bending, posterior bending, and torsion) with Finite Element Analysis (FEA) using Ansys Workbench 2020 R2. Mechanical testing was conducted to validate the FEA results, A simple model consisting of a single ring, two K-wires, and polylactic acid (PLA) cylinders was utilized in a tensile test. It has been concluded from the results that the parallel model and its improvement have higher stiffness to axial compression, medial bending, and torsion, but a lower posterior bending stiffness, except the divergent model with 8-hole separation which has a relatively acceptable stiffness for posterior bending.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140710277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-15DOI: 10.3991/ijoe.v20i05.47247
Cara Dannenberg, Johannes Heimann, A. Koumpis, O. Beyan
As part of the Europe-wide smart health innovation hub implemented in the context of the Horizon Europe SHIFT-HUB project, our work concerns the identification of specific medical research ecosystems in the two fields, namely cardiovascular diseases and mental illness, with Cologne as the central location. To achieve this aim, the websites of involved organizations were used for data research purposes, and the members of each respective ecosystem or network were identified by acquiring information about their cooperation partners. A variety of selection criteria have been applied to filter out whether these partners were suitable to be considered as a further starting point for the research. The results indicate the existence of ecosystems in the two fields, with Cologne as the central location, in which various stakeholders, including healthcare institutions, healthcare providers, foundations, NGOs, and the business community, work closely together. Larger institutions are usually networked at an international level, while smaller institutions increasingly depend on and foster regional partnerships. This promotes cooperation and the exchange of knowledge at the regional level and facilitates direct contact with the people affected, i.e., patients’ groups. Research institutions in both fields often receive financial support from commercial organizations, which highlights the importance of the business community’s involvement in exploiting research results and promoting the quality of healthcare. The article highlights the complexity and interdisciplinarity of the particular ecosystems, with all the different categories of institutions comprising an indispensable position. The interaction amongst stakeholders at international, regional, and local levels can significantly help to deploy resources more effectively and improve the quality of life of people suffering from any of the two conditions.
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