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| 001 | 102913 | ||
| 003 | DE-He213 | ||
| 005 | 20230102113105.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 171022s2018 si | s |||| 0|eng d | ||
| 020 | _a9789811056840 | ||
| 024 | 7 |
_a10.1007/978-981-10-5684-0 _2doi |
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| 040 |
_aES-MaUEC _bspa |
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| 050 | 4 |
_aTK5103.2 _bL466 2018 EB |
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| 100 | 1 |
_aLeMoyne, Robert _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/106205010/ _9100424 |
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| 245 | 1 | 0 |
_aWearable and Wireless Systems for Healthcare I _bGait and Reflex Response Quantification _cby Robert LeMoyne, Timothy Mastroianni. |
| 264 | 1 |
_aSingapore _bSpringer International Publishing _c2018 |
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| 300 | _a1 recurso en línea (XIV, 134 páginas 34 ilustraciones, 24 ilustraciones a color) | ||
| 347 |
_atext file _bPDF |
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| 490 | 0 |
_aSmart Sensors, Measurement and Instrumentation _x2194-8402 _v27 |
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| 505 | 0 | _aWearable and wireless systems for gait analysis and reflex quantification -- Traditional clinical evaluation of gait and reflex response by ordinal scale -- Quantification systems appropriate for a clinical setting -- The rise of inertial measurement units -- Portable wearable and wireless systems for gait and reflex response quantification -- Smartphones and portable media devices as wearable and wireless systems for gait and reflex response quantification -- Bluetooth inertial sensors for gait and reflex response quantification with perspectives regarding Cloud Computing and the Internet of Things -- Quantifying the spatial position representation of gait through sensor fusion -- Role of machine learning for gait and reflex response classification -- Homebound therapy with wearable and wireless systems -- Future perspective of Network Centric Therapy. | |
| 520 | 3 | _aThis book provides visionary perspective and interpretation regarding the role of wearable and wireless systems for the domain of gait and reflex response quantification. These observations are brought together in their application to smartphones and other portable media devices to quantify gait and reflex response in the context of machine learning for diagnostic classification and integration with the Internet of things and cloud computing. The perspective of this book is from the first-in-the-world application of these devices, as in smartphones, for quantifying gait and reflex response, to the current state of the art. Dr. LeMoyne has published multiple groundbreaking applications using smartphones and portable media devices to quantify gait and reflex response. | |
| 650 | 7 |
_aSistemas de comunicación inalámbricos _2embne _9158044 |
|
| 650 | 7 |
_aFisiología humana _2embne _9140024 |
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| 700 | 1 |
_aMastroianni, Timothy _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n2018185122 _1http://viaf.org/viaf/6152986585612670669/ |
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| 776 | 0 | 8 |
_iEdición impresa: _z9789811056833 |
| 776 | 0 | 8 |
_iEdición impresa: _z9789811056857 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-10-5684-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 490 | 0 | _aEngineering (Springer-11647) | |
| 988 | _aEBSPRINGER_2018 | ||
| 998 |
_b02/2019 _dz _ek _feng _ggw _h0 |
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| 999 |
_c102913 _d102913 _x1 |
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