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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Academic Press,
[2018]
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Ai, Qingsong, |e author. | |
| 245 | 1 | 0 | |a Advanced rehabilitative technology : |b neural interfaces and devices / |c Qingsong Ai, Quan Liu, Wei Meng, Sheng Quan Xie. |
| 264 | 1 | |a London : |b Academic Press, |c [2018] | |
| 300 | |a 1 online resource : |b color illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed August 17, 2018). | |
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 520 | |a 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 establish the neural interfaces, and how to develop neural devices and control them efficiently using biological signals. The book takes a multidisciplinary theme between the engineering and medical field, including sections on neuromuscular/brain signal processing, human motion and intention recognition, biomechanics modelling and interfaces, and neural devices and control for rehabilitation. Each chapter goes through a detailed description of the bio-mechatronic systems used and then presents implementation and testing tactics. In addition, it details new neural interfaces and devices, some of which have never been published before in any journals or conferences. With this book, readers will quickly get up-to-speed on the most recent and future advancements in bio-mechatronics engineering for applications in rehabilitation. | ||
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| 650 | 2 | |a Pattern Recognition, Physiological |0 (DNLM)D046709 | |
| 650 | 2 | |a Signal Processing, Computer-Assisted |0 (DNLM)D012815 | |
| 650 | 2 | |a Biosensing Techniques |0 (DNLM)D015374 | |
| 650 | 2 | |a Electronics, Medical |0 (DNLM)D004582 | |
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| 700 | 1 | |a Liu, Quan, |e author. | |
| 700 | 1 | |a Meng, Wei, |e author. | |
| 700 | 1 | |a Xie, Sheng Quan, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Ai, Qingsong. |t Advanced rehabilitative technology. |d London : Academic Press, [2018] |z 0128145978 |z 9780128145975 |w (OCoLC)1010623228 |
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