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008			170204s2017 sz ob 000 0 eng d
020			_a3319528831
020			_a331952884X _q(electronic bk.)
020			_a9783319528830
020			_a9783319528847 _q(electronic bk.)
020			_z9783319528830 _q(print)
035			_a(OCoLC)971365124 _z(OCoLC)974649935 _z(OCoLC)981099326 _z(OCoLC)981813846 _z(OCoLC)1005810184 _z(OCoLC)1011999213
040			_aEBLCP _cEBLCP _dGW5XE _dN$T _dOCLCQ _dYDX _dOCLCO _dIDEBK _dOCLCF _dUAB _dOCLCO _dCOO _dUPM _dCNCGM _dIOG _dOCLCO _dVT2 _dUWO _dMERER _dESU _dOCLCO _dJBG _dIAD _dOCLCO _dICW _dOCLCO _dICN _dOCLCO _dOTZ _dOCLCQ _dOCLCA _dOCLCQ _dOCLCO _dOCLCA _dU3W _dOCLCO _dES-MaUEC _bspa
050		4	_aR856 _b.X547 2017 EB
100	1		_aXie, Shane. _997018
245	1	0	_aBiomechatronics in medical rehabilitation : _bbiomodelling, interface, and control _cShane Xie, Wei Meng.
264		1	_aCham _bSpringer _c2017
300			_a1 recurso en línea (214 páginas)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
500			_a8.5.2 Robot Experiments and Results.
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas
505	0		_aPreface; Acknowledgements; Contents; Nomenclature; 1 Introduction; 1.1 Medical Background and Requirements; 1.2 BCI Systems; 1.3 EMG-Based Neuromuscular Interface; 1.4 Human-Robot Interaction Control; 1.5 Summary; References; 2 State of the Art; 2.1 EEG-Based BCI and Its Challenges; 2.1.1 Steady State Visual Evoked Potentials; 2.1.2 EEG Signal Processing: Improving the SNR; 2.1.3 EEG Signal Processing: Signal Translation and Classification; 2.1.4 Current Limitations; 2.2 EMG and the Neuromuscular Interface; 2.2.1 Applications of sEMG; 2.2.2 sEMG-Based Neuromuscular Interface.
505	8		_a2.2.3 Current Challenges2.3 Neuromusculoskeletal Models for Gait Rehabilitation; 2.3.1 Musculoskeletal Model; 2.3.2 EMG-Driven Models; 2.4 Discussion; 2.5 Summary; References; 3 Signal Processing Methods for SSVEP-Based BCIs; 3.1 Introduction; 3.2 Adjacent Narrow Band Filter (ANBF) Algorithm; 3.2.1 Artefact Reduction; 3.2.2 Frequency Recognition Strategy; 3.3 Methods and Materials; 3.3.1 Experimental Protocol; 3.3.2 EEG Recording and Evaluation; 3.4 Results; 3.5 Discussion; 3.6 Summary; References; 4 SSVEP-Based BCI for Lower Limb Rehabilitation; 4.1 Introduction; 4.2 Methods and Materials.
505	8		_a4.2.1 Subjects and Visual Stimulator4.2.2 SSVEP Signal Processing; 4.2.3 Robotic Exoskeleton Device; 4.2.4 Experimental Protocols; 4.2.5 Control Algorithm; 4.3 Results; 4.4 Discussion; 4.5 Summary; References; 5 A Hybrid BCI for Gaming; 5.1 Introduction; 5.2 BCI Setup; 5.2.1 Signal Recording and Processing; 5.2.2 Super Street Fighter Video Game; 5.3 Experimental Method and Results; 5.3.1 Experimental Protocol; 5.3.2 Results; 5.4 Discussion; 5.5 Summary; References; 6 EMG-Driven Physiological Model for Upper Limb; 6.1 Neuromusculoskeletal Model; 6.1.1 Musculoskeletal Geometry Model.
505	8		_a6.1.2 Musculotendon Model6.1.3 Kinematic Model; 6.2 Model Sensitivity Analysis; 6.2.1 Model Parameters; 6.2.2 Sensitivity Analysis; 6.2.3 Results and Discussion; 6.3 Elbow Physiological Model Validation; 6.3.1 Experimental Setup; 6.3.2 Model Validation; 6.4 Summary; References; 7 Exoskeleton Control Based on Neural Interface; 7.1 Exoskeleton Development; 7.2 Exoskeleton Control; 7.2.1 Control System Design; 7.2.2 Control of the Elbow Joint; 7.3 Human-Robot Interface; 7.3.1 Interface Design and Parameter Tuning; 7.3.2 Graphical User Interface; 7.4 Summary; References.
505	8		_a8 Muscle Force Estimation Model for Gait Rehabilitation8.1 Patient-Specific Muscle Force Estimation; 8.1.1 Patient-Specific Musculoskeletal Model; 8.1.2 Inverse Dynamic Modelling; 8.1.3 Static Optimisation; 8.2 PMFE Evaluation and Results; 8.2.1 PMFE Evaluation; 8.2.2 Simulation Results; 8.2.3 Discussion; 8.3 Human-Inspired Robotic Exoskeleton; 8.4 Biological Command Based Controller; 8.4.1 Dynamic Modelling; 8.4.2 Patient-Specific Muscle Force Estimation; 8.4.3 PMFE Based Feedforward Controller; 8.5 PSBc Evaluation and Results; 8.5.1 Computer Simulation and Results.
520	3		_aThis book focuses on the key technologies in developing biomechatronic systems for medical rehabilitation purposes. It includes a detailed analysis of biosignal processing, biomechanics modelling, neural and muscular interfaces, artificial actuators, robot-assisted training, clinical setup/implementation and rehabilitation robot control. Encompassing highly multidisciplinary themes in the engineering and medical fields, it presents researchers? insights into the emerging technologies and developments that are being utilized in biomechatronics for medical purposes. Presenting a detailed analysis of five key areas in rehabilitation robotics: (i) biosignal processing; (ii) biomechanics modelling; (iii) neural and muscular interfaces; (iv) artificial actuators and devices; and (v) the use of neurological and muscular interfaces in rehabilitation robots control, the book describes the design of biomechatronic systems, the methods and control systems used and the implementation and testing in order to show how they fulfil the needs of that specific area of rehabilitation. Providing a comprehensive overview of the background of biomechatronics and details of new advances in the field, it is especially useful for researchers, academics and graduates new to the field of biomechatronics engineering, and is also of interest to researchers and clinicians in the medical field who are not engineers.
650		7	_9496877 _aMecatrónica _2fast _0(OCoLC)fst01013514 _0
700	1		_aMeng, Wei.
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-52884-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017B
998			_b02/2018 _dz _e- _zSI
999			_c95321 _d95321 _x1