"Computational Intelligence in Electromyography Analysis: A Perspective..." ed. by Ganesh R. Naik

This book presents an updated overview of signal processing applications and recent developments in Electromyography (EMG) from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms.

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists.
This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research.

Contents
Preface
Section 1 EMG Modelling
1 EMG Modeling
2 Modelling of Transcrania I Magnetic Stimulation in One-Year Follow-Up Study of Patients with Minor Ischaemic Stroke
3 Relationships Between Surface Electromyography and Strength During Isometric Ramp Contractions
4 Comparison by EMG of Running Barefoot and Running Shod
5 Influence of Different Strategies of Treatment Muscle Contraction and Relaxation Phases on EMG Signal Processing and Analysis During Cyclic Exercise
Section 2 EMG Analysis and Applications
6 Nonlinear Analysis of Surface EMG Signals
7 Normalization of EMG Signals: To Normalize or Not to Normalize and What to Normalize to?
8 The Usefulness of Mean and Median Frequencies in Electromyography Analysis
9 Feature Extraction Methods for Studying Surface Electromyography and Kinematic Measurements in Parkinson's Disease
10 Distinction of Abnormality of Surgical Operation on the Basis of Surface EMG Signals
11 EMG Decomposition and Artefact Removal
12 Sphincter EMG for Diagnosing Multiple System Atrophy and Related Disorders
Section 3 EMG Applications: Hand Gestures and Prosthetics
13 Hand Sign Classification Employing Myoelectric Signals of Forearm
14 Proposal of a Neuro Fuzzy System for Myoelectric Signal Analysis from Hand-Arm Segment
15 Design and Control of an EMG Driven IPMC Based Artificial Muscle Finger
16 Application of Surface Electromyography in the Dynamics of Human Moviment
17 Virtual and Augmented Reality: A New Approach to Aid Users of Myoelectric Prostheses
18 Signal Acquisition Using Surface EMG and Circuit Design Considerations for Robotic Prosthesis

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