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| 020 | _a9783030387488 | ||
| 024 | 7 |
_a10.1007/978-3-030-38748-8 _2doi |
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_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC |
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| 041 | 0 | _aeng | |
| 050 | 4 |
_aR857.B54 _b2020 EB |
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_aChallenges and Trends in Multimodal Fall Detection for Healthcare _cedited by Hiram Ponce, Lourdes Martínez-Villaseñor, Jorge Brieva, Ernesto Moya-Albor. |
| 250 | _aFirst edition | ||
| 264 | 1 |
_aCham _bSpringer International Publishing : _bImprint Springer _c2020 |
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| 300 | _a1 recurso en línea (XIII, 259 páginas) | ||
| 336 |
_2rdacontent _aTexto _btxt |
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| 337 |
_2rdamedia _aelectrónico _bc |
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| 338 |
_2rdacarrier _arecurso electrónico _bcr |
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| 347 |
_atext file _bPDF |
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| 490 | 0 |
_aStudies in Systems Decision and Control _x2198-4182 _v273 |
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| 505 | 0 | _aChallenges and Solutions on Human Fall Detection and Classification -- Open Source Implementation for Fall Classification and Fall Detection Systems -- Detecting Human Activities based on a Multimodal Sensor Data Set using a Bidirectional Long Short-Term Memory Model: A Case Study -- Approaching Fall Classification using the UP-Fall Detection Dataset: Analysis and Results from an International Competition -- Reviews and Trends on Multimodal Healthcare -- A Novel Approach for Human Fall Detection and Fall Risk Assessment. | |
| 520 | 3 | _aThis book focuses on novel implementations of sensor technologies, artificial intelligence, machine learning, computer vision and statistics for automated, human fall recognition systems and related topics using data fusion. It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state of the art in human fall and activity recognition systems, while the second part describes a public dataset especially curated for multimodal fall detection. It also gathers contributions demonstrating the use of this dataset and showing examples. This book is useful for anyone who is interested in fall detection systems, as well as for those interested in solving challenging, signal recognition, vision and machine learning problems. Potential applications include health care, robotics, sports, human-machine interaction, among others. | |
| 988 | _aPrimersemestre_2020_Engineering | ||
| 650 | 7 |
_2embne _aBiosensores _9158891 |
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| 650 | 7 |
_2embne _aBiomecánica _9143819 |
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| 700 | 1 |
_aPonce, Hiram _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 700 | 1 |
_aMartínez-Villaseñor, Lourdes _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
| 700 | 1 |
_aBrieva, Jorge _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 700 | 1 |
_aMoya-Albor, Ernesto _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 773 | 0 | _tSpringer eBooks | |
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_iPrinted edition: _z9783030387471 |
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_iPrinted edition: _z9783030387495 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030387501 |
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_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-38748-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
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_b03/2020 _dz _eb _zSI |
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