EAI Endorsed Transactions on Pervasive Health and Technology最新文献

{"title":"Evaluation and Monitoring System for Exercise Rehabilitation Based on Combined Chinese and Western Medicine Technology","authors":"Yunxiang Shang","doi":"10.4108/eetpht.10.5138","DOIUrl":"https://doi.org/10.4108/eetpht.10.5138","url":null,"abstract":"INTRODUCTION: Developing exercise rehabilitation assessment and monitoring systems is essential in rehabilitation medicine. Introduces a sports rehabilitation assessment and monitoring system based on combining Chinese and Western medicine technology, aiming to integrate traditional Chinese and Western medicine theory and modern Western medicine technology to provide more comprehensive and personalized rehabilitation services. Through the systematic integration of technologies, the author is committed to building an efficient and precise rehabilitation system to provide patients with more scientific and practical rehabilitation programs. OBJECTIVE: The research system employs various sensor technologies, including motion capture devices, biosensors, and pulse recognition technology, by combining Chinese and Western medicine. The motion capture device enables real-time monitoring of the patient's movement trajectory, joint mobility, and other physiological indicators; the biosensor collects the patient's physiological data, such as heart rate and blood pressure. Meanwhile, Chinese and Western medicine pulse recognition technology was introduced to obtain pulse information specific to Chinese and Western medicine to provide more comprehensive data support for rehabilitation assessment. Integrating these technologies, a multi-level and multi-dimensional rehabilitation assessment system was established. METHODS: This study aims to improve the accuracy and personalization of the rehabilitation assessment and to tailor a rehabilitation plan more in line with the patient's actual situation. Through the combination of Chinese and Western medicine techniques, it aims to break the single perspective of traditional rehabilitation assessment and make the rehabilitation plan closer to the physiological characteristics and pathological state of the patients as well as the needs of the combination of Chinese and Western medicine in the identification and treatment. RESULTS: The system has achieved remarkable results in practical application. The accurate monitoring of the motion capture device provides a more comprehensive understanding of the patient's motor status and accurately analyzes the rehabilitation progress. At the same time, the data collection of biosensors provides doctors with more detailed physiological information, enabling them to formulate rehabilitation plans more comprehensively. The introduction of combined Chinese and Western medicine pulse recognition technology adds a unique auxiliary diagnostic tool of collaborative Chinese and Western medicine to the rehabilitation assessment and improves the personalized level of the rehabilitation plan. CONCLUSION: The sports rehabilitation assessment and monitoring system combining Chinese and Western medicine technology brings new ideas and methods for developing rehabilitation medicine. By fully utilizing the advantages of modern technology and traditional medical knowledge, a comprehensive and in","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"112 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140091119","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}

{"title":"Analysis on Smart Healthcare Monitoring Based on Compound Dimension","authors":"B. Vennilapriya, C. Bennila Thangammal","doi":"10.4108/eetpht.10.5266","DOIUrl":"https://doi.org/10.4108/eetpht.10.5266","url":null,"abstract":"INTRODUCTION: Life expectancy has steadily increased in the majority of countries over the last few decades as a result of vast improvements in medical care, public health initiatives, and individual, community hygiene practices as well. \u0000OBJECTIVES: An effective and inexpensive alternative to institutional care was remote health surveillance, which relies on non-invasive and wearable sensors, actuators, and modern statement and information technology to allow the elderly to remain in their familiar homes. \u0000METHODS: With the use of open-source software, widely accessible minimal chipsets, and remote data warehouses for storing, this study details the design and construction of e-health apparel for health monitoring. \u0000RESULTS: By utilizing these devices, medical professionals will be able to track vital signs in real-time, evaluate patients' status, and provide feedback even when they are physically located in a different facility. The next step included creating a wearable system and the garment platform it would be used on. \u0000CONCLUSION: More features were implemented in the form of a smartphone application. This research has potential application in broadening the scope of wearable healthcare systems by investigating the role of apparel in this area.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"18 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084906","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}

{"title":"Detection of Female Anopheles Mosquito-Infected Cells: Exploring CNN, ReLU, and Sigmoid Activation Methods","authors":"A. L. Leena Jenifer, B. K. Indumathi, C. P. Mahalakshmi","doi":"10.4108/eetpht.10.5269","DOIUrl":"https://doi.org/10.4108/eetpht.10.5269","url":null,"abstract":"INTRODUCTION: Deep learning uses multi-layer neural networks where the algorithm decides for itself whether aspects are essential for analysis based on the raw input. In general, deep learning networks get better as more data is used to train them. For a variety of applications, convolutional neural networks are frequently used to analyse, categorize, and detect images. \u0000OBJECTIVES: The proposed system technique is used for automated analysis of malaria-detecting frameworks. A female Anopheles mosquito bite is the primary method of transmission of the blood disease malaria. It is still common to manually count and identify parasitized cells during microscopic examination of either thick or thin layers of haemoglobin, which takes time for disease prognosis. \u0000METHODS: The current research uses a neural network based on convolution to catalogue images of cells with and without malaria infection. This method improves the precision of classification for the datasets under study. The ReLu activation function used by this model enables it to learn more quickly and perform more effectively. \u0000RESULTS: The prediction of infected and healthy cells was done accurately by the proposed model, which uses only 3 layers of convolution, and this was the idea behind the implementation. The model achieved an improved accuracy of 99.77% across 12 iterations (epochs). \u0000CONCLUSION: The proposed model is straightforward and successful in differentiating between malaria-infected and uninfected cells.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"55 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140085588","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}

{"title":"Speckle Noise Removal from Biomedical MRI Images and Classification by Multi-Support Vector Machine","authors":"B. Hemalatha, B. Karthik, C. V. Krishna Reddy","doi":"10.4108/eetpht.10.5076","DOIUrl":"https://doi.org/10.4108/eetpht.10.5076","url":null,"abstract":"INTRODUCTION: Image Processing (IP) methods play a vital role in medical images for diagnosing and predicting illness, as well as monitoring the patient's progress. The IP methods are utilized in many applications for example in the field of medicine. \u0000OBJECTIVES: The images that are obtained by the MRI magnetic Resonance imaging and x rays are analyzed with the help of image processing. \u0000METHODS: This application is very costly to the patient. Because of the several non-idealities in the image process, medical images are frequently tainted by impulsive, multiplicative, and addictive noise. \u0000RESULTS: By replacing some of the original image's pixels with new ones that have luminance values which are less than the allowed dynamic luminance range, noise frequently affects medical images. \u0000CONCLUSION: In this research work, the Speckle type noises are eliminated with the help of Mean Filter (MF) and classify the images using Multi-SVM classifier.  The entire system developed using python programming.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"10 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139853707","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}

{"title":"Speckle Noise Removal from Biomedical MRI Images and Classification by Multi-Support Vector Machine","authors":"B. Hemalatha, B. Karthik, C. V. Krishna Reddy","doi":"10.4108/eetpht.10.5076","DOIUrl":"https://doi.org/10.4108/eetpht.10.5076","url":null,"abstract":"INTRODUCTION: Image Processing (IP) methods play a vital role in medical images for diagnosing and predicting illness, as well as monitoring the patient's progress. The IP methods are utilized in many applications for example in the field of medicine. \u0000OBJECTIVES: The images that are obtained by the MRI magnetic Resonance imaging and x rays are analyzed with the help of image processing. \u0000METHODS: This application is very costly to the patient. Because of the several non-idealities in the image process, medical images are frequently tainted by impulsive, multiplicative, and addictive noise. \u0000RESULTS: By replacing some of the original image's pixels with new ones that have luminance values which are less than the allowed dynamic luminance range, noise frequently affects medical images. \u0000CONCLUSION: In this research work, the Speckle type noises are eliminated with the help of Mean Filter (MF) and classify the images using Multi-SVM classifier.  The entire system developed using python programming.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"71 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139793939","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}

{"title":"Detection of Lung and Colon Cancer using Average and Weighted Average Ensemble Models","authors":"Hemalatha Gunasekaran, S. D. Kanmani, Shamila Ebenezer, Wilfred Blessing, K. Ramalakshmi","doi":"10.4108/eetpht.10.5017","DOIUrl":"https://doi.org/10.4108/eetpht.10.5017","url":null,"abstract":"INTRODUCTION: Cancer is a life-threatening condition triggered by metabolic irregularities or the convergence of hereditary disorders. Cancerous cells in lung and colon leads more death rate count in the human race today. The histological diagnosis of malignant cancers is critical in establishing the most appropriate treatment for patients. Detecting cancer in its early stages, before it has a chance to advance within the body, greatly reduces the risk of death in both cases. \u0000OBJECTIVES: In order to examine a larger patient group more efficiently and quickly, researchers can utilize different methods of machine learning approach and different models of deep learning used to speed up the detection of cancer. \u0000METHODS: In this work, we provide a new ensemble transfer learning model for the rapid detection of lung and colon cancer. By ingtegrating various models of transfer learning approach and combining these methods in an ensemble, we aim to enhance the overall performance of the diagnosis process. \u0000RESULTS: The outcomes of this research indicate that our suggested approach performs better than current models, making it a valuable tool for clinics to support medical personnel in more efficiently detecting lung and colon cancer. \u0000CONCLUSION: The average ensemble is able to reach an accuracy of 98.66%, while the weighted-average ensemble with an accuracy of 99.80%, which is good with analysis of existing approaches.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"28 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139863198","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}

{"title":"Detection of Lung and Colon Cancer using Average and Weighted Average Ensemble Models","authors":"Hemalatha Gunasekaran, S. D. Kanmani, Shamila Ebenezer, Wilfred Blessing, K. Ramalakshmi","doi":"10.4108/eetpht.10.5017","DOIUrl":"https://doi.org/10.4108/eetpht.10.5017","url":null,"abstract":"INTRODUCTION: Cancer is a life-threatening condition triggered by metabolic irregularities or the convergence of hereditary disorders. Cancerous cells in lung and colon leads more death rate count in the human race today. The histological diagnosis of malignant cancers is critical in establishing the most appropriate treatment for patients. Detecting cancer in its early stages, before it has a chance to advance within the body, greatly reduces the risk of death in both cases. \u0000OBJECTIVES: In order to examine a larger patient group more efficiently and quickly, researchers can utilize different methods of machine learning approach and different models of deep learning used to speed up the detection of cancer. \u0000METHODS: In this work, we provide a new ensemble transfer learning model for the rapid detection of lung and colon cancer. By ingtegrating various models of transfer learning approach and combining these methods in an ensemble, we aim to enhance the overall performance of the diagnosis process. \u0000RESULTS: The outcomes of this research indicate that our suggested approach performs better than current models, making it a valuable tool for clinics to support medical personnel in more efficiently detecting lung and colon cancer. \u0000CONCLUSION: The average ensemble is able to reach an accuracy of 98.66%, while the weighted-average ensemble with an accuracy of 99.80%, which is good with analysis of existing approaches.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"19 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139803500","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}