Preface

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. Since the contracting skeletal muscles are greatly responsible for loading of the bones and joints, information about the muscle EMG is important to gain knowledge about muscular-skeletal biomechanics. Myoelectric signals can also demonstrate the development of loading imbalance and asymmetry, which in turn relates to physical disability. 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 and – aside from muscular activity - EMG can be used to indicate and quantify the development of muscle fatigue.

A great challenge in biomedical engineering is to non-invasively assess the physiological changes occurring in different internal organs of the human body. These variations can be modeled and measured often as biomedical source signals that indicate the function or malfunction of various physiological systems. To extract the relevant information for diagnosis and therapy, expert knowledge in medicine and engineering is required. Biomedical source signals, especially EMG, are usually weak, stationary signals and distorted by noise and interference. Moreover, they are usually mutually superimposed. Besides classical signal analysis tools (such as adaptive supervised filtering, parametric or non-parametric spectral estimation, time frequency analysis, and higher order statistics), Intelligent Signal Processing techniques are used for pre-processing, noise and artefact reduction, enhancement, detection and estimation of EMG signals by taking into account their spatio-temporal correlation and mutual statistical dependence.

This book is aimed to provide a self-contained introduction to the subject as well as offering a set of invited contributions, which we see as lying at the cutting edge of both empirical and computational aspects of EMG research. This book was born from discussions with researchers in the EMG community and aims to provide a snapshot of some current trends and future challenges in EMG research.

Book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in

#### X Preface

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. Furthermore, the research results previously scattered in many scientific journals and conference papers worldwide, are methodically collected and presented in the book in a unified form. The book is likely to be of interest to graduate and postgraduate students, neurologists, engineers and scientists - in the field of neural signal processing and biomedical engineering. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research. Although these chapters can be read almost independently, they share the same notations and the same subject index. Moreover, numerous cross- references link the chapters to each other.

As an Editor and also an Author in this field, I am privileged to be editing a book with such fascinating topics, written by a selected group of gifted researchers. I would like to extend my gratitude to the authors, who have committed so much effort to the publication of this book.

> **Dr. Ganesh R. Naik**  RMIT University, Melbourne, Australia

X Preface

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. Furthermore, the research results previously scattered in many scientific journals and conference papers worldwide, are methodically collected and presented in the book in a unified form. The book is likely to be of interest to graduate and postgraduate students, neurologists, engineers and scientists - in the field of neural signal processing and biomedical engineering. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research. Although these chapters can be read almost independently, they share the same notations and the same subject index. Moreover,

As an Editor and also an Author in this field, I am privileged to be editing a book with such fascinating topics, written by a selected group of gifted researchers. I would like to extend my gratitude to the authors, who have committed so much effort to the

> **Dr. Ganesh R. Naik**  RMIT University, Melbourne, Australia

numerous cross- references link the chapters to each other.

publication of this book.

**Section 1** 

**EMG Modelling** 

**Section 1** 
