04057nam a22005535i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001400172072001700186072002300203072001600226100008200242245015300324250001800477264006900495300006500564336002600629337002600655338003600681347002400717490005100741505065100792520129501443650002902738650003202767650002802799650002902827650004902856650002102905650002902926650002402955650002802979650005803007650007103065700008503136710003403221773002603255776003603281776003603317830005803353856004603411912001403457912001503471999001703486978-3-031-45003-7DE-He21320251030110830.0cr nn 008mamaa231105s2024    sz |    s    |||| 0|eng d  a97830314500379978-3-031-45003-77 a10.1007/978-3-031-45003-72doi 4aQ325.5-.7 7aUYQM2bicssc 7aMAT0290002bisacsh 7aUYQM2thema1 aChanna, Asma.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut96702910aDeep Learning in Smart eHealth Systemsh[electronic resource] :bEvaluation Leveraging for Parkinson’s Disease /cby Asma Channa, Nirvana Popescu.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXIII, 94 p. 35 illus., 33 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringerBriefs in Computer Science,x2191-57760 aUnraveling Parkinson’s Disease: Diagnostic Challenges and Severity Assessment -- State-of-the-Art: Wearable Devices and Deep Learning Techniques for Parkinson’s Disease -- Design and Engineering of a Medical Wearable Device for Parkinson’s Disease Management -- Deep Learning Models for Parkinson’s Disease Severity Evaluation -- Transforming Parkinson’s Disease Care: Cloud Service Empowered by ServiceNow Technology -- Predicting Wearing-Off Episodes in Parkinson’s with Multimodal Machine Learning -- Enhancing Gait Analysis Through Wearable Insoles and Deep Learning Techniques -- Conclusion and Prospects for Further Development.  aOne 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 researchers, practitioners, and students interested in the intersection of technology and healthcare. 0aMachine learning.955059 0aMedical informatics.960693 0aCloud Computing.958386 0aComputer science.958552 0aImage processingxDigital techniques.964228 0aComputer vision.14aMachine Learning.95505924aHealth Informatics.24aCloud Computing.95838624aTheory and Algorithms for Application Domains.96703024aComputer Imaging, Vision, Pattern Recognition and Graphics.9655501 aPopescu, Nirvana.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut947962 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303145002008iPrinted edition:z9783031450044 0aSpringerBriefs in Computer Science,x2191-577696657840uhttps://doi.org/10.1007/978-3-031-45003-7  aZDB-2-SCS  aZDB-2-SXCS  c56508d56508