Advanced rehabilitative technology : neural interfaces and devices /

Advanced Rehabilitative Technology: Neural Interfaces and Devices teaches readers how to acquire and process bio-signals using biosensors and acquisition devices, how to identify the human movement intention and decode the brain signal, how to design physiological and musculoskeletal models and esta...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Ai, Qingsong (Autor), Liu, Quan (Autor), Meng, Wei (Autor), Xie, Sheng Quan (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, [2018]
Temas:
Nervous system > Rehabilitation > Technological innovations.
Rehabilitation technology.
Medical rehabilitation > Technological innovations.
Biosensors.
Medical electronics.
Neuroinformatics.
Self-help devices for people with disabilities.
Rehabilitation > instrumentation
Robotics > methods
Brain-Computer Interfaces
Self-Help Devices
Pattern Recognition, Physiological
Signal Processing, Computer-Assisted
Biosensing Techniques
Electronics, Medical
Technologie de la r�eadaptation.
R�eadaptation > Innovations.
Biocapteurs.
�Electronique en m�edecine.
Neuro-informatique.
Aides fonctionnelles (M�edecine physique)
HEALTH & FITNESS > Diseases > General.
MEDICAL > Clinical Medicine.
MEDICAL > Diseases.
MEDICAL > Evidence-Based Medicine.
MEDICAL > Internal Medicine.
Self-help devices for people with disabilities
Biosensors
Medical electronics
Neuroinformatics
Rehabilitation technology
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover; Advanced Rehabilitative Technology: Neural Interfaces and Devices; Copyright; Contents; Author Biography; Preface; Acknowledgments; Chapter 1: Introduction; 1.1. Background; 1.2. Human Biological Systems; 1.3. Neural Interfaces and Devices; 1.4. Critical Issues; 1.5. Chapter Summary; References; Further Reading; Chapter 2: State-of-the-Art; 2.1. Neuromuscular Signal; 2.1.1. EMG Signal Acquisition; 2.1.2. EMG Signal Processing; Signal Preprocessing; Feature Extraction; Pattern Recognition; Postprocessing; 2.1.3. Applications of Neuromuscular Signal; Discrete Movement Recognition
  • Continuous Movement Recognition2.2. Brain Signal; 2.2.1. Fundamentals of EEG Electrophysiology; 2.2.2. Composition and Characteristics of EEG Signals; Spontaneous and Rhythmic Properties of EEG; EEG Has Small Amplitude and Low Frequency; EEG Signal Source Has High Internal Resistance and Randomness; 2.2.3. Types and Characteristics of EEG Signals; Visual Evoked Potential; Slow Cortical Potential; P300 Potential; Alpha Waves Produced by Eye Movements; EEG Signals Based on Motor Imagery; 2.3. Neural Modeling and Interfaces; 2.4. Chapter Summary; References
  • Chapter 3: Neuromuscular Signal Acquisition and Processing3.1. sEMG Signal; 3.1.1. Production of sEMG Signal; 3.1.2. Characteristics of sEMG Signals; 3.2. sEMG Acquisition Devices; 3.2.1. Requirement of sEMG Acquisition; 3.2.2. Wired sEMG Acquisition Device; Design and Implementation of Acquisition Device; Performance Test; 3.2.3. WiFi-Based sEMG Acquisition Device; Design and Implementation of Acquisition Device; Performance Test; 3.2.4. Bluetooth-Based sEMG Acquisition Device; Design and Implementation of the Acquisition Device; Performance Test; 3.2.5. DataLOG Product
  • 3.3. sEMG Signal Preprocessing3.3.1. Wavelet Analysis-Based sEMG Denoising; Wavelet Denoising; Wavelet Packet Denoising; Best Wavelet Packet Adaptive Threshold Denoising; Comparison Between Methods; 3.3.2. Singular Spectrum-Based sEMG Denoising; 3.4. Chapter Summary; References; Chapter 4: sEMG-Based Motion Recognition; 4.1. sEMG Feature Extraction and Classification; 4.1.1. sEMG Feature Extraction Methods; Time Domain Analysis; Frequency Domain Analysis; Time-Frequency Domain Analysis; High-Order Spectral Analysis; Nonlinear Dynamic Analysis; 4.1.2. sEMG Pattern Recognition Methods
  • Cluster AnalysisArtificial Neural Networks; Support Vector Machines; Fuzzy Pattern Recognition; 4.2. Hand Gesture Recognition; 4.2.1. Best Wavelet Package Denoising for Preprocessing; 4.2.2. Wavelet Coefficient and LLE for Feature Extraction; Extraction of Wavelet Coefficients; Extraction of the LLE; Construction of Joint Feature; 4.2.3. BP Neural Network for Classification; 4.2.4. Experimental Results Analysis; 4.3. Ankle Motion Recognition; 4.3.1. Feature Extraction and Selection; 4.3.2. LS_SVM for Classification; Classification Method; 4.3.3. Experimental Results and Analysis

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