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派博传思国际中心 »期刊书目

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Titlebook: Deep Learning in Smart eHealth Systems; Evaluation Leveragin Asma Channa,Nirvana Popescu Book 2024 The Editor(s) (if applicable) and The Au

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发表于 2025-3-21 18:28:58 | 显示全部楼层 |阅读模式
书目名称Deep Learning in Smart eHealth Systems
副标题Evaluation Leveragin
编辑Asma Channa,Nirvana Popescu
视频videohttp://file.papertrans.cn/265/264628/264628.mp4
概述Combines state-of-the-art technology with clinical expertise to develop a personalized and efficient evaluation.Presents a new eHealth framework using Deep Learning and IoT wearable devices to assess
丛书名称SpringerBriefs in Computer Science
图书封面Titlebook: Deep Learning in Smart eHealth Systems; Evaluation Leveragin Asma Channa,Nirvana Popescu Book 2024 The Editor(s) (if applicable) and The Au
描述.One of the main benefits of this book is that it presents a comprehensive and innovative eHealth framework that leverages deep learning and IoT wearable devices for the evaluation of Parkinson‘s disease patients. This framework offers a new way to assess and monitor patients‘ motor deficits in a personalized and automated way, improving the efficiency and accuracy of diagnosis and treatment..Compared to other books on eHealth and Parkinson‘s disease, this book offers a unique perspective and solution to the challenges facing patients and healthcare providers. It combines state-of-the-art technology, such as wearable devices and deep learning algorithms, with clinical expertise to develop a personalized and efficient evaluation framework for Parkinson‘s disease patients..This book provides a roadmap for the integration of cutting-edge technology into clinical practice, paving the way for more effective and patient-centered healthcare. To understand this book, readers should have a basic knowledge of eHealth, IoT, deep learning, and Parkinson‘s disease. However, the book provides clear explanations and examples to make the content accessible to a wider audience, including researcher
出版日期Book 2024
关键词A-WEAR Bracelet; Cloud Platform; Continuous Wavelet Transform; Convolutional Neural Network; Deep Learni
版次1
doihttps://doi.org/10.1007/978-3-031-45003-7
isbn_softcover978-3-031-45002-0
isbn_ebook978-3-031-45003-7Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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,Design and Engineering of a Medical Wearable Device for Parkinson’s Disease Management,of PD stages and disease progression, especially concerning tremor and bradykinesia. This chapter endeavors to craft a holistic ecosystem capable of capturing motion data associated with PD and securely transmitting it to the cloud for storage, data processing, and severity estimation, all facilitat
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,Deep Learning Models for Parkinson’s Disease Severity Evaluation,ignals are analyzed using the CatBoost classifier. This approach yields an impressive accuracy rate of 96%. The findings of this study substantially contribute to the development of a reliable and accurate framework for assessing PD severity.
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Conclusion and Prospects for Further Development,nd support offered to prevailing theories and models in the field of PD monitoring. It highlights the creation of an objective monitoring system for assessing PD symptoms, which employs meticulous preprocessing, feature extraction, and model evaluation techniques.
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Determinanten der Demokratiezufriedenheitof PD stages and disease progression, especially concerning tremor and bradykinesia. This chapter endeavors to craft a holistic ecosystem capable of capturing motion data associated with PD and securely transmitting it to the cloud for storage, data processing, and severity estimation, all facilitat
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2191-5768 work using Deep Learning and IoT wearable devices to assess .One of the main benefits of this book is that it presents a comprehensive and innovative eHealth framework that leverages deep learning and IoT wearable devices for the evaluation of Parkinson‘s disease patients. This framework offers a ne
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