With the development of network technology and intelligent application platforms, the "Three Precision" applet as a method of individual intervention for students' physical fitness can not only enable students to obtain the improvement of physical fitness and lifelong sports habits, but also establish a new bridge of cooperation between home and school. The analysis method of student physical fitness individual intervention assessment is affected by a variety of factors such as the framework design of the WeChat applet platform and the subjectivity of the intervention, which leads to the inefficiency of the student physical fitness individual intervention assessment method. To address this problem, we analyse the mode and content of students' physical fitness individual intervention based on the "Three Precision" applet, extract the feature vectors of students' physical fitness individual intervention, construct a system of students' physical fitness individual intervention assessment indexes, and establish a method of students' physical fitness individual intervention assessment based on big data technology and WeChat applet by combining the mushroom propagation optimization algorithm and convolutional neural network. Individual intervention assessment method based on big data technology and WeChat applet. The effectiveness and robustness of the proposed method are verified by using the data recorded in the "Three Precision" applet as the input data of the model. The results show that the proposed method meets the real-time requirements and improves the prediction accuracy of the individual intervention assessment method, which significantly improves the efficiency of the individual intervention assessment of students' physical fitness.
{"title":"Individual Intervention and Assessment of Students' Physical Fitness Based on the \"Three Precision\" Applet and Mixed Strategy Optimised CNN Networks","authors":"Daomeng Zhang","doi":"10.4108/eetpht.10.5852","DOIUrl":"https://doi.org/10.4108/eetpht.10.5852","url":null,"abstract":"With the development of network technology and intelligent application platforms, the \"Three Precision\" applet as a method of individual intervention for students' physical fitness can not only enable students to obtain the improvement of physical fitness and lifelong sports habits, but also establish a new bridge of cooperation between home and school. The analysis method of student physical fitness individual intervention assessment is affected by a variety of factors such as the framework design of the WeChat applet platform and the subjectivity of the intervention, which leads to the inefficiency of the student physical fitness individual intervention assessment method. To address this problem, we analyse the mode and content of students' physical fitness individual intervention based on the \"Three Precision\" applet, extract the feature vectors of students' physical fitness individual intervention, construct a system of students' physical fitness individual intervention assessment indexes, and establish a method of students' physical fitness individual intervention assessment based on big data technology and WeChat applet by combining the mushroom propagation optimization algorithm and convolutional neural network. Individual intervention assessment method based on big data technology and WeChat applet. The effectiveness and robustness of the proposed method are verified by using the data recorded in the \"Three Precision\" applet as the input data of the model. The results show that the proposed method meets the real-time requirements and improves the prediction accuracy of the individual intervention assessment method, which significantly improves the efficiency of the individual intervention assessment of students' physical fitness.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"88 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101020","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}
As a carrier of college students' health status monitoring, portable intelligent terminal APP, the study of its APP application analysis not only provides a new way for college students' extracurricular physical exercise, guides college students to actively participate in extracurricular physical activities using intelligent terminal APP software, but also promotes college students' physical health monitoring and enhancement in various aspects. Aiming at the current portable terminal APP college students' health monitoring application analysis method research exists low precision, real-time poor and other problems, through the analysis of the basic functional framework and functional characteristics of the portable intelligent terminal APP, the establishment of the portable intelligent terminal APP analysis index system applied to college students' health monitoring, combined with the heuristic optimisation algorithm and the improvement of deep learning algorithms, the construction of the marine predator based heuristic optimisation algorithm and the attention mechanism to improve the gating control loop. Combining the heuristic optimisation algorithm and the improved deep learning algorithm, we construct the portable intelligent terminal APP application analysis method for college students' health monitoring based on the marine predator heuristic optimisation algorithm and the attention mechanism improved gated recurrent unit neural network. Through simulation analysis, the results show that the proposed method meets the real-time requirements while improving the prediction accuracy of the portable smart terminal APP application analysis method, and significantly improves the efficiency of portable smart terminal APP analysis.
{"title":"Research on Portable Intelligent Terminal and APP Application Analysis and Intelligent Monitoring Method of College Students' Health Status","authors":"Yu Li, Yuetong Gao","doi":"10.4108/eetpht.10.5899","DOIUrl":"https://doi.org/10.4108/eetpht.10.5899","url":null,"abstract":"As a carrier of college students' health status monitoring, portable intelligent terminal APP, the study of its APP application analysis not only provides a new way for college students' extracurricular physical exercise, guides college students to actively participate in extracurricular physical activities using intelligent terminal APP software, but also promotes college students' physical health monitoring and enhancement in various aspects. Aiming at the current portable terminal APP college students' health monitoring application analysis method research exists low precision, real-time poor and other problems, through the analysis of the basic functional framework and functional characteristics of the portable intelligent terminal APP, the establishment of the portable intelligent terminal APP analysis index system applied to college students' health monitoring, combined with the heuristic optimisation algorithm and the improvement of deep learning algorithms, the construction of the marine predator based heuristic optimisation algorithm and the attention mechanism to improve the gating control loop. Combining the heuristic optimisation algorithm and the improved deep learning algorithm, we construct the portable intelligent terminal APP application analysis method for college students' health monitoring based on the marine predator heuristic optimisation algorithm and the attention mechanism improved gated recurrent unit neural network. Through simulation analysis, the results show that the proposed method meets the real-time requirements while improving the prediction accuracy of the portable smart terminal APP application analysis method, and significantly improves the efficiency of portable smart terminal APP analysis. ","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"81 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101889","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}
Naara Medina-Altamirano, Duliano Ramirez-Morales, Darwin Gutierrez-Alamo, Jose Rojas-Diaz, Wilver Ticona-Larico5, Cynthia López-Gómez
INTRODUCTION: Muscle warm-up plays a fundamental role before developing any physical activity because it allows the body to prepare to perform better in physical activity, being a process that is carried out through a series of moderate intensity exercises that result in an increase gradual reduction of muscle and body temperature, avoiding possible injuries or muscle pain. Therefore, muscle warm-up is an essential activity mainly in those sports where greater force is exerted on the legs, being the part of the body where injuries such as ankle sprains or knee injuries are commonly seen that lead to painful and uncomfortable injuries for students-athletes.OBJECTIVES: Develop a thermal image processing system to monitor the muscle warm-up of students prior to their sports activities to evaluate the state of the muscle warm-up of the leg part and prevent damage or injuries, as well as the indication of requiring another additional muscle warm-up to determine a correct muscle warm-up.METHODS: The proposed method involves the use of thermal images to monitor muscle warm-up before and after physical activity. In addition, the use of MATLAB software to analyze the images and compare the status of muscle warm-up.RESULTS: Through the development of this proposed system, its operation was appreciated with an efficiency of 95.97% in monitoring the muscle warm-up of the students prior to their physical activities achieved through image processing.CONCLUSION: It is concluded that the proposed system is effective in monitoring muscle warm-up and preventing injuries in student-athletes.
{"title":"Thermal image processing system to monitor muscle warm-up in students prior to their sports activities","authors":"Naara Medina-Altamirano, Duliano Ramirez-Morales, Darwin Gutierrez-Alamo, Jose Rojas-Diaz, Wilver Ticona-Larico5, Cynthia López-Gómez","doi":"10.4108/eetpht.10.5888","DOIUrl":"https://doi.org/10.4108/eetpht.10.5888","url":null,"abstract":"INTRODUCTION: Muscle warm-up plays a fundamental role before developing any physical activity because it allows the body to prepare to perform better in physical activity, being a process that is carried out through a series of moderate intensity exercises that result in an increase gradual reduction of muscle and body temperature, avoiding possible injuries or muscle pain. Therefore, muscle warm-up is an essential activity mainly in those sports where greater force is exerted on the legs, being the part of the body where injuries such as ankle sprains or knee injuries are commonly seen that lead to painful and uncomfortable injuries for students-athletes.OBJECTIVES: Develop a thermal image processing system to monitor the muscle warm-up of students prior to their sports activities to evaluate the state of the muscle warm-up of the leg part and prevent damage or injuries, as well as the indication of requiring another additional muscle warm-up to determine a correct muscle warm-up.METHODS: The proposed method involves the use of thermal images to monitor muscle warm-up before and after physical activity. In addition, the use of MATLAB software to analyze the images and compare the status of muscle warm-up.RESULTS: Through the development of this proposed system, its operation was appreciated with an efficiency of 95.97% in monitoring the muscle warm-up of the students prior to their physical activities achieved through image processing.CONCLUSION: It is concluded that the proposed system is effective in monitoring muscle warm-up and preventing injuries in student-athletes.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100600","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}
Animation techniques have been completely transformed by the union of Artificial Intelligence (AI) and biomechanical modeling, particularly in 2D animation. This study looks at a combination of AI and biomechanics to address the challenges of simulating 2D animation. Current approaches in 2D animation often struggle to achieve lifelike and fluid movements, especially when representing complex motion or interaction. These traditional techniques rely on manual keyframing or physics simulation, which may be time-consuming and do not provide the rich detail needed for realism in animations. To meet these aspects, this study suggested 2D animation using Artificial Intelligence with Biomechanical Modeling (2D-AI-BM). Our approach thus harnesses Deep Neural Network (DNN) for moving forecasts and improvement using biopsychological principles to help us imitate natural human actions better. In addition to character animation, it could apply to interactive storytelling and educational simulations. As a result, animators get more control over motion generation while drastically reducing the necessity for manual intervention through this fusion of AI and biomechanics, which smoothens the production pipeline for animations. This paper considers several important metrics to evaluate the proposed approach’s effectiveness, including user satisfaction, computational efficiency, motion smoothness and realism. Comparative studies with classical animation methods showed that the method generates realistic movements on 2D characters while saving time during production. The numerical findings exemplify that the recommended 2D-AI-BM model improves an accuracy rate of 97.4%, computational efficiency ratio of 96.3%, motion control ratio of 95.4%, pose detection ratio of 94.8% and scalability ratio of 93.2% compared to other popular techniques.
{"title":"Research on 2D Animation Simulation Based on Artificial Intelligence and Biomechanical Modeling","authors":"Fangming Dai, Zhiyong Li","doi":"10.4108/eetpht.10.5907","DOIUrl":"https://doi.org/10.4108/eetpht.10.5907","url":null,"abstract":"Animation techniques have been completely transformed by the union of Artificial Intelligence (AI) and biomechanical modeling, particularly in 2D animation. This study looks at a combination of AI and biomechanics to address the challenges of simulating 2D animation. Current approaches in 2D animation often struggle to achieve lifelike and fluid movements, especially when representing complex motion or interaction. These traditional techniques rely on manual keyframing or physics simulation, which may be time-consuming and do not provide the rich detail needed for realism in animations. To meet these aspects, this study suggested 2D animation using Artificial Intelligence with Biomechanical Modeling (2D-AI-BM). Our approach thus harnesses Deep Neural Network (DNN) for moving forecasts and improvement using biopsychological principles to help us imitate natural human actions better. In addition to character animation, it could apply to interactive storytelling and educational simulations. As a result, animators get more control over motion generation while drastically reducing the necessity for manual intervention through this fusion of AI and biomechanics, which smoothens the production pipeline for animations. This paper considers several important metrics to evaluate the proposed approach’s effectiveness, including user satisfaction, computational efficiency, motion smoothness and realism. Comparative studies with classical animation methods showed that the method generates realistic movements on 2D characters while saving time during production. The numerical findings exemplify that the recommended 2D-AI-BM model improves an accuracy rate of 97.4%, computational efficiency ratio of 96.3%, motion control ratio of 95.4%, pose detection ratio of 94.8% and scalability ratio of 93.2% compared to other popular techniques.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"3 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141105212","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}
INTRODUCTION: Skin cancer is emerging as a significant health risk. Melanoma, a perilous kind of skin cancer, prominently manifests asymmetry in its morphological characteristics. OBJECTIVE: The objective of the study is to classify the asymmetry of the skin lesion shape accurately and to find the number of symmetric lines and the angles of formation of symmetric lines. METHOD: This study introduces a unique methodology known as Shape Asymmetry Analysis (SAA). The SAA incorporates a comprehensive framework including image pre-processing, segmentation along with the computation of mean deviation error and the subsequent categorization of data into symmetric and asymmetric forms using a classification model. RESULT: The PH2 dataset is used in this study, where the three labels are consolidated into two categories. Specifically, the labels "symmetric" and "symmetric with one axis" are merged and classified as "symmetric," while the label "asymmetric" is unchanged and classified as "asymmetric". The model demonstrates superior performance compared to conventional methodologies, achieving a noteworthy accuracy rate of 90%. Additionally, it exhibits a weighted F1-score, precision, and recall of 0.89,0.91,0.90 respectively. CONCLUSION: The SAA model accurately classifies skin lesion shapes compared to state-of-the-art methods. The model can be applied to the shapes, irrespective of irregularity, to find symmetric lines and angles.
{"title":"SAA: A novel skin lesion Shape Asymmetry Classification Analysis","authors":"Shaik Reshma, Reeja S R","doi":"10.4108/eetpht.10.5580","DOIUrl":"https://doi.org/10.4108/eetpht.10.5580","url":null,"abstract":"INTRODUCTION: Skin cancer is emerging as a significant health risk. Melanoma, a perilous kind of skin cancer, prominently manifests asymmetry in its morphological characteristics. \u0000OBJECTIVE: The objective of the study is to classify the asymmetry of the skin lesion shape accurately and to find the number of symmetric lines and the angles of formation of symmetric lines. \u0000METHOD: This study introduces a unique methodology known as Shape Asymmetry Analysis (SAA). The SAA incorporates a comprehensive framework including image pre-processing, segmentation along with the computation of mean deviation error and the subsequent categorization of data into symmetric and asymmetric forms using a classification model. \u0000RESULT: The PH2 dataset is used in this study, where the three labels are consolidated into two categories. Specifically, the labels \"symmetric\" and \"symmetric with one axis\" are merged and classified as \"symmetric,\" while the label \"asymmetric\" is unchanged and classified as \"asymmetric\". The model demonstrates superior performance compared to conventional methodologies, achieving a noteworthy accuracy rate of 90%. Additionally, it exhibits a weighted F1-score, precision, and recall of 0.89,0.91,0.90 respectively. \u0000CONCLUSION: The SAA model accurately classifies skin lesion shapes compared to state-of-the-art methods. The model can be applied to the shapes, irrespective of irregularity, to find symmetric lines and angles.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"31 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372686","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}
INTRODUCTION: The SARS-COV-2 pandemic has led to a significant increase in the number of infected individuals and a considerable loss of lives. Identifying SARS-COV-2-induced pneumonia cases promptly is crucial for controlling the virus's spread and improving patient care. In this context, chest X-ray imaging has become an essential tool for detecting pneumonia caused by the novel coronavirus. OBJECTIVES: The primary goal of this research is to differentiate between pneumonia cases induced specifically by the SARS-COV-2 virus and other types of pneumonia or healthy cases. This distinction is vital for the effective treatment and isolation of affected patients. METHODS: A streamlined stacked Convolutional Neural Network (CNN) architecture was employed for this study. The dataset, meticulously curated from Johns Hopkins University's medical database, comprised 2292 chest X-ray images. This included 542 images of COVID-19-infected cases and 1266 non-COVID cases for the training phase, and 167 COVID-infected images plus 317 non-COVID images for the testing phase. The CNN's performance was assessed against a well-established CNN model to ensure the reliability of the findings. RESULTS: The proposed CNN model demonstrated exceptional accuracy, with an overall accuracy rate of 98.96%. In particular, the model achieved a per-class accuracy of 99.405% for detecting SARS-COV-2-infected cases and 98.73% for identifying non-COVID cases. These results indicate the model's significant potential in distinguishing between COVID-19-related pneumonia and other conditions. CONCLUSION: The research validates the efficacy of using a specialized CNN architecture for the rapid and precise identification of SARS-COV-2-induced pneumonia from chest X-ray images. The high accuracy rates suggest that this method could be a valuable tool in the ongoing fight against the COVID-19 pandemic, aiding in the swift diagnosis and effective treatment of patients.
{"title":"Swift Diagnose: A High-Performance Shallow Convolutional Neural Network for Rapid and Reliable SARS-COV-2 Induced Pneumonia Detection","authors":"Koustav Dutta, Rasmita Lenka, Priya Gupta, Aarti Goel, Janjhyam Venkata Naga Ramesh","doi":"10.4108/eetpht.10.5581","DOIUrl":"https://doi.org/10.4108/eetpht.10.5581","url":null,"abstract":"INTRODUCTION: The SARS-COV-2 pandemic has led to a significant increase in the number of infected individuals and a considerable loss of lives. Identifying SARS-COV-2-induced pneumonia cases promptly is crucial for controlling the virus's spread and improving patient care. In this context, chest X-ray imaging has become an essential tool for detecting pneumonia caused by the novel coronavirus. \u0000OBJECTIVES: The primary goal of this research is to differentiate between pneumonia cases induced specifically by the SARS-COV-2 virus and other types of pneumonia or healthy cases. This distinction is vital for the effective treatment and isolation of affected patients. \u0000METHODS: A streamlined stacked Convolutional Neural Network (CNN) architecture was employed for this study. The dataset, meticulously curated from Johns Hopkins University's medical database, comprised 2292 chest X-ray images. This included 542 images of COVID-19-infected cases and 1266 non-COVID cases for the training phase, and 167 COVID-infected images plus 317 non-COVID images for the testing phase. The CNN's performance was assessed against a well-established CNN model to ensure the reliability of the findings. \u0000RESULTS: The proposed CNN model demonstrated exceptional accuracy, with an overall accuracy rate of 98.96%. In particular, the model achieved a per-class accuracy of 99.405% for detecting SARS-COV-2-infected cases and 98.73% for identifying non-COVID cases. These results indicate the model's significant potential in distinguishing between COVID-19-related pneumonia and other conditions. \u0000CONCLUSION: The research validates the efficacy of using a specialized CNN architecture for the rapid and precise identification of SARS-COV-2-induced pneumonia from chest X-ray images. The high accuracy rates suggest that this method could be a valuable tool in the ongoing fight against the COVID-19 pandemic, aiding in the swift diagnosis and effective treatment of patients.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"105 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370456","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}
Reeja S R, Sangameswar J, Solomon Joseph Joju, Mrudhul Reddy Gangula, Sujith S
INTRODUCTION: This work represents a significant step forward by harnessing the power of deep learning to classify X-ray images into distinct body parts. Over the years X-ray pictures were evaluated manually. OBJECTIVE: Our aim is to automate X-ray interpretation using deep learning techniques. METHOD: Leveraging cutting-edge frameworks such as FastAI and TensorFlow, a Convolutional Neural Network (CNN) has been meticulously trained on a dataset comprising DICOM images and their corresponding labels. RESULT: The results achieved by the model are indeed promising, as it demonstrates a remarkable ability to accurately identify various body parts. CNN shows 97.38% performance by compared with other classifiers. CONCLUSION: This innovation holds the potential to revolutionize medical diagnosis and treatment planning through the automation of image analysis, marking a substantial leap forward in the field of healthcare technology.
简介：这项工作利用深度学习的强大功能，将 X 光图像分类为不同的身体部位，是向前迈出的重要一步。多年来，X 射线图片都是人工评估的。目标：我们的目标是利用深度学习技术实现 X 光解读的自动化。方法：利用 FastAI 和 TensorFlow 等尖端框架，在由 DICOM 图像及其相应标签组成的数据集上对卷积神经网络（CNN）进行了细致的训练。结果：该模型所取得的结果确实令人欣喜，因为它展示了准确识别身体各部位的非凡能力。与其他分类器相比，CNN 的性能达到了 97.38%。结论：这一创新有望通过图像分析自动化彻底改变医疗诊断和治疗计划，标志着医疗保健技术领域的重大飞跃。
{"title":"X-ray body Part Classification Using Custom CNN","authors":"Reeja S R, Sangameswar J, Solomon Joseph Joju, Mrudhul Reddy Gangula, Sujith S","doi":"10.4108/eetpht.10.5577","DOIUrl":"https://doi.org/10.4108/eetpht.10.5577","url":null,"abstract":"INTRODUCTION: This work represents a significant step forward by harnessing the power of deep learning to classify X-ray images into distinct body parts. Over the years X-ray pictures were evaluated manually. \u0000OBJECTIVE: Our aim is to automate X-ray interpretation using deep learning techniques. \u0000METHOD: Leveraging cutting-edge frameworks such as FastAI and TensorFlow, a Convolutional Neural Network (CNN) has been meticulously trained on a dataset comprising DICOM images and their corresponding labels. \u0000RESULT: The results achieved by the model are indeed promising, as it demonstrates a remarkable ability to accurately identify various body parts. CNN shows 97.38% performance by compared with other classifiers. \u0000CONCLUSION: This innovation holds the potential to revolutionize medical diagnosis and treatment planning through the automation of image analysis, marking a substantial leap forward in the field of healthcare technology. ","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"83 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371068","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}
INTRODUCTION: Health information amassed during the treatment of a medical condition is termed health data. This data encompasses information gathered about a patient and their family, forming a patient history. The internet has progressively transformed communication, commerce, and information acquisition. Among the diverse domains it has influenced, the healthcare sector stands out as one of the most intricate and unique realms of integration. Big data are the results of normal online transactions and interactions that take place online, the detectors that are implanted in devices and actual locations, as well as the generation of digital contents by individuals whenever they submit data over internet. OBJECTIVES: The need of protection of health data and methods of safeguarding patient privacy. The study also helps understand and appreciate the best practices which will help India in implementing the law more effectively. METHODS: A doctrinal method of research was employed to analyse the laws and regulations. A comparative approach of different countries gives us the understanding of the gaps and issues. The efficacy of the laws was tested as the paper explores the laws of Canada & Indonesia regarding data protection. RESULTS: In this study, we understood the generation, processing, and interchange of these massive amounts of data can now be facilitated by cloud computing technology. As India, recently enacted ‘The Digital Data Protection Act 2023’ which might be a ray of hope for protection of sensitive health data of individuals from misuse. CONCLUSION: The journey towards optimal data protection is ongoing, requiring continuous adaptation to the dynamic nature of technology and the ever-changing healthcare environment.
{"title":"Safeguarding Patient Privacy: Exploring Data Protection in E-Health Laws: A Cross-Country Analysis","authors":"Sambhabi Patnaik, Kyvalya Garikapati, Lipsa Dash, Ramyani Bhattacharya, Arpita Mohapatra","doi":"10.4108/eetpht.10.5583","DOIUrl":"https://doi.org/10.4108/eetpht.10.5583","url":null,"abstract":"INTRODUCTION: Health information amassed during the treatment of a medical condition is termed health data. This data encompasses information gathered about a patient and their family, forming a patient history. The internet has progressively transformed communication, commerce, and information acquisition. Among the diverse domains it has influenced, the healthcare sector stands out as one of the most intricate and unique realms of integration. Big data are the results of normal online transactions and interactions that take place online, the detectors that are implanted in devices and actual locations, as well as the generation of digital contents by individuals whenever they submit data over internet. \u0000OBJECTIVES: The need of protection of health data and methods of safeguarding patient privacy. The study also helps understand and appreciate the best practices which will help India in implementing the law more effectively. \u0000METHODS: A doctrinal method of research was employed to analyse the laws and regulations. A comparative approach of different countries gives us the understanding of the gaps and issues. The efficacy of the laws was tested as the paper explores the laws of Canada & Indonesia regarding data protection. \u0000RESULTS: In this study, we understood the generation, processing, and interchange of these massive amounts of data can now be facilitated by cloud computing technology. As India, recently enacted ‘The Digital Data Protection Act 2023’ which might be a ray of hope for protection of sensitive health data of individuals from misuse. \u0000CONCLUSION: The journey towards optimal data protection is ongoing, requiring continuous adaptation to the dynamic nature of technology and the ever-changing healthcare environment.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371906","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}
Poonam Mittal, S. P. Abirami, Puppala Ramya, Balajee J, Elangovan Muniyandy
INTRODUCTION: COVID-19 was declared as most dangerous disease and even after maintaining so many preventive measures, vaccination is the only preventive option from SARS-CoV-2. Vaccination has controlled the risk and spreading of virus that causes COVID-19. Vaccines can help in preventing serious illness and death. Before recommendation of COVID-19 vaccines, clinical experiments are being conducted with thousands of grown person and children. In controlled situations like clinical trials, efficacy refers to how well a vaccination prevents symptomatic or asymptomatic illness. OBJECTIVES: The effectiveness of a vaccine relates to how effectively it works in the actual world. METHODS: This research presents a novel approach to model the efficacy of COVID’19 vaccines based on Mamdani Fuzzy system Modelling. The proposed fuzzy model aims to gauge the impact of epidemiological and clinical factors on which the efficacy of COVID’19 vaccines. RESULTS: In this study, 8 different aspects are considered, which are classified as efficiency evaluating factors. To prepare this model, data has been accumulated from various research papers, reliable news articles on vaccine response in multiple regions, published journals etc. A set of Fuzzy rules was inferred based on classified parameters. This fuzzy inference system is expected to be of great help in recommending the most appropriate vaccine on the basis of several parameters. CONCLUSION: It aims to give an idea to pharmaceutical manufacturers on how they can improve vaccine efficacy and for the decision making that which one to be followed.
{"title":"Rule Based Mamdani Fuzzy Inference System to Analyze Efficacy of COVID19 Vaccines","authors":"Poonam Mittal, S. P. Abirami, Puppala Ramya, Balajee J, Elangovan Muniyandy","doi":"10.4108/eetpht.10.5571","DOIUrl":"https://doi.org/10.4108/eetpht.10.5571","url":null,"abstract":"INTRODUCTION: COVID-19 was declared as most dangerous disease and even after maintaining so many preventive measures, vaccination is the only preventive option from SARS-CoV-2. Vaccination has controlled the risk and spreading of virus that causes COVID-19. Vaccines can help in preventing serious illness and death. Before recommendation of COVID-19 vaccines, clinical experiments are being conducted with thousands of grown person and children. In controlled situations like clinical trials, efficacy refers to how well a vaccination prevents symptomatic or asymptomatic illness. \u0000OBJECTIVES: The effectiveness of a vaccine relates to how effectively it works in the actual world. \u0000METHODS: This research presents a novel approach to model the efficacy of COVID’19 vaccines based on Mamdani Fuzzy system Modelling. The proposed fuzzy model aims to gauge the impact of epidemiological and clinical factors on which the efficacy of COVID’19 vaccines. \u0000RESULTS: In this study, 8 different aspects are considered, which are classified as efficiency evaluating factors. To prepare this model, data has been accumulated from various research papers, reliable news articles on vaccine response in multiple regions, published journals etc. A set of Fuzzy rules was inferred based on classified parameters. This fuzzy inference system is expected to be of great help in recommending the most appropriate vaccine on the basis of several parameters. \u0000CONCLUSION: It aims to give an idea to pharmaceutical manufacturers on how they can improve vaccine efficacy and for the decision making that which one to be followed.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"96 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140377320","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}
INTRODUCTION: Epilepsy denotes a disorder of neurological origin marked by repetitive and spontaneous seizures without any apparent trigger. Seizures occur due to abrupt and heightened electricity flowing through the brain, which can lead to physical and mental symptoms. There are several types of epileptic seizures, and epilepsy itself can be caused by various underlying conditions. EEG (Electroencephalogram) is one of the most important and widely used tools for epileptic seizure prediction and diagnosis. EEG uses skull sensors to record electrical signals from the brain., and it can provide valuable insights into brain activity patterns associated with seizures. OBJECTIVES: Brain-computer interface technology pathway for analyzing the EEG signals for seizure prediction to eliminate the class imbalance issue from our dataset in this case, a SMOTE approach is applied. It is observable that there are more classes of one variable than there are of the others in the output variable. This will be problematic when employing different Artificial intelligence techniques since these algorithms are more likely to be biased towards a certain variable because of its high prevalence METHODS: SMOTE approaches will be used to address this bias and balance the number of variables in the response variable. To develop an XGBoost (Extreme Gradient Boosting) model using SMOTE techniques to increase classification accuracy. RESULTS: The results show that the XGBoost method achieves a 98.7% accuracy rate. CONCLUSION: EEG-based model for seizure type using the XGBoost model for predicting the disease early. The Suggested method could significantly reduce the amount of time needed to accomplish seizure prediction.
{"title":"Forecasting Epileptic Seizures Using XGBoost Methodology and EEG Signals","authors":"S. Mounika, Reeja S R","doi":"10.4108/eetpht.10.5569","DOIUrl":"https://doi.org/10.4108/eetpht.10.5569","url":null,"abstract":"INTRODUCTION: Epilepsy denotes a disorder of neurological origin marked by repetitive and spontaneous seizures without any apparent trigger. Seizures occur due to abrupt and heightened electricity flowing through the brain, which can lead to physical and mental symptoms. There are several types of epileptic seizures, and epilepsy itself can be caused by various underlying conditions. EEG (Electroencephalogram) is one of the most important and widely used tools for epileptic seizure prediction and diagnosis. EEG uses skull sensors to record electrical signals from the brain., and it can provide valuable insights into brain activity patterns associated with seizures. \u0000OBJECTIVES: Brain-computer interface technology pathway for analyzing the EEG signals for seizure prediction to eliminate the class imbalance issue from our dataset in this case, a SMOTE approach is applied. It is observable that there are more classes of one variable than there are of the others in the output variable. This will be problematic when employing different Artificial intelligence techniques since these algorithms are more likely to be biased towards a certain variable because of its high prevalence \u0000METHODS: SMOTE approaches will be used to address this bias and balance the number of variables in the response variable. To develop an XGBoost (Extreme Gradient Boosting) model using SMOTE techniques to increase classification accuracy. \u0000RESULTS: The results show that the XGBoost method achieves a 98.7% accuracy rate. \u0000CONCLUSION: EEG-based model for seizure type using the XGBoost model for predicting the disease early. The Suggested method could significantly reduce the amount of time needed to accomplish seizure prediction.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"15 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140375795","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}
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