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COMPUTATIONAL INTELLIGENCE IN ELECTROMYOGRAPHY ANALYSIS – A PERSPECTIVE ON CURRENT APPLICATIONS AND FUTURE CHALLENGES

ELECTROMYOGRAPHY ANALYSIS
Edited by Ganesh R. Naik .

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 
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

CONTENTS :

Section 1 EMG Modelling .

Chapter 1 EMG Modeling 3
Javier Rodriguez-Falces, Javier Navallas and Armando Malanda

Chapter 2 Modelling of Transcranial Magnetic Stimulation in One-Year Follow-Up Study of Patients with Minor Ischaemic Stroke 37
Penka A. Atanassova, Nedka T. Chalakova and Borislav D. Dimitrov

Chapter 3 Relationships Between Surface Electromyography and Strength During Isometric Ramp Contractions 53
Runer Augusto Marson

Chapter 4 Comparison by EMG of Running Barefoot and Running Shod 65
Begoña Gavilanes-Miranda, Juan J. Goiriena De Gandarias and Gonzalo A. Garcia

Chapter 5 Influence of Different Strategies of Treatment Muscle Contraction and Relaxation Phases on EMG Signal Processing and Analysis During Cyclic Exercise 97
Leandro Ricardo Altimari, José Luiz Dantas, Marcelo Bigliassi, Thiago Ferreira Dias Kanthack, Antonio Carlos de Moraes and Taufik Abrão

Section 2 EMG Analysis and Applications .

Chapter 6 Nonlinear Analysis of Surface EMG Signals 119
Min Lei and Guang Meng

Chapter 7 Normalization of EMG Signals: To Normalize or Not to Normalize and What to Normalize to? 175
Mark Halaki and Karen Ginn

Chapter 8 The Usefulness of Mean and Median Frequencies in Electromyography Analysis 195
Angkoon Phinyomark, Sirinee Thongpanja, Huosheng Hu, Pornchai Phukpattaranont and Chusak Limsakul

Chapter 9 Feature Extraction Methods for Studying Surface Electromyography and Kinematic Measurements in Parkinson’s Disease 221
Saara M. Rissanen, Markku Kankaanpää, Mika P. Tarvainen and Pasi A. Karjalainen

Chapter 10 Distinction of Abnormality of Surgical Operation on the Basis of Surface EMG Signals 
Chiharu Ishii

Chapter 11 EMG Decomposition and Artefact Removal 261
Adriano O. Andrade, Alcimar B. Soares, Slawomir J. Nasuto and Peter J. Kyberd

Chapter 12 Sphincter EMG for Diagnosing Multiple System Atrophy and Related Disorders 287
Ryuji Sakakibara, Tomoyuki Uchiyama, Tatsuya Yamamoto, Fuyuki Tateno, Tomonori Yamanishi, Masahiko Kishi and Yohei Tsuyusaki

Section 3 EMG Applications: Hand Gestures and Prosthetics .

Chapter 13 Hand Sign Classification Employing Myoelectric Signals of Forearm 309
Takeshi Tsujimura, Sho Yamamoto and Kiyotaka Izumi

Chapter 14 Proposal of a Neuro Fuzzy System for Myoelectric Signal Analysis from Hand-Arm Segment 337
Gabriela Winkler Favieiro and Alexandre Balbinot

Chapter 15 Design and Control of an EMG Driven IPMC Based Artificial Muscle Finger 363
R.K. Jain, S. Datta and S. Majumder

Chapter 16 Application of Surface Electromyography in the Dynamics of Human Movement 391
César Ferreira Amorim and Runer Augusto Marson

Chapter 17 Virtual and Augmented Reality: A New Approach to Aid Users of Myoelectric Prostheses 409
Alcimar Barbosa Soares, Edgard Afonso Lamounier Júnior, Adriano de Oliveira Andrade and Alexandre Cardoso

Chapter 18 Signal Acquisition Using Surface EMG and Circuit Design Considerations for Robotic Prosthesis 427
Muhammad Zahak Jamal



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