000 | 04222nam a22006015i 4500 | ||
---|---|---|---|
001 | 978-3-031-45003-7 | ||
003 | DE-He213 | ||
005 | 20240730170439.0 | ||
007 | cr nn 008mamaa | ||
008 | 231105s2024 sz | s |||| 0|eng d | ||
020 |
_a9783031450037 _9978-3-031-45003-7 |
||
024 | 7 |
_a10.1007/978-3-031-45003-7 _2doi |
|
050 | 4 | _aQ325.5-.7 | |
072 | 7 |
_aUYQM _2bicssc |
|
072 | 7 |
_aMAT029000 _2bisacsh |
|
072 | 7 |
_aUYQM _2thema |
|
082 | 0 | 4 |
_a006.31 _223 |
100 | 1 |
_aChanna, Asma. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _993619 |
|
245 | 1 | 0 |
_aDeep Learning in Smart eHealth Systems _h[electronic resource] : _bEvaluation Leveraging for Parkinson's Disease / _cby Asma Channa, Nirvana Popescu. |
250 | _a1st ed. 2024. | ||
264 | 1 |
_aCham : _bSpringer Nature Switzerland : _bImprint: Springer, _c2024. |
|
300 |
_aXIII, 94 p. 35 illus., 33 illus. in color. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
||
347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aSpringerBriefs in Computer Science, _x2191-5776 |
|
505 | 0 | _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. | |
520 | _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. | ||
650 | 0 |
_aMachine learning. _91831 |
|
650 | 0 |
_aMedical informatics. _94729 |
|
650 | 0 |
_aCloud Computing. _94659 |
|
650 | 0 |
_aComputer science. _99832 |
|
650 | 0 |
_aImage processing _xDigital techniques. _94145 |
|
650 | 0 |
_aComputer vision. _993622 |
|
650 | 1 | 4 |
_aMachine Learning. _91831 |
650 | 2 | 4 |
_aHealth Informatics. _931799 |
650 | 2 | 4 |
_aCloud Computing. _94659 |
650 | 2 | 4 |
_aTheory and Algorithms for Application Domains. _979177 |
650 | 2 | 4 |
_aComputer Imaging, Vision, Pattern Recognition and Graphics. _931569 |
700 | 1 |
_aPopescu, Nirvana. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _993624 |
|
710 | 2 |
_aSpringerLink (Online service) _993626 |
|
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031450020 |
776 | 0 | 8 |
_iPrinted edition: _z9783031450044 |
830 | 0 |
_aSpringerBriefs in Computer Science, _x2191-5776 _993628 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-45003-7 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cEBK | ||
999 |
_c86953 _d86953 |