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COMPUTATIONAL
INTELLIGENCE IN
ELECTROMYOGRAPHY
ANALYSIS –
A PERSPECTIVE ON
CURRENT APPLICATIONS
AND FUTURE CHALLENGES
Edited by Ganesh R. Naik
Computational Intelligence in Electromyography Analysis –
A Perspective on Current Applications and Future Challenges
http://dx.doi.org/10.5772/3315
Edited by Ganesh R. Naik
Contributors
Javier Rodriguez-Falces, Javier Navallas, Armando Malanda, Penka A. Atanassova, Nedka T.
Chalakova, Borislav D. Dimitrov, Runer Augusto Marson, Begoña Gavilanes-Miranda, Juan J.
Goiriena De Gandarias, Gonzalo A. Garcia, Leandro Ricardo Altimari, José Luiz Dantas,
Marcelo Bigliassi, Thiago Ferreira Dias Kanthack, Antonio Carlos de Moraes, Taufik Abrão,
Min Lei, Guang Meng, Mark Halaki, Karen Ginn, Angkoon Phinyomark, Sirinee Thongpanja,
Huosheng Hu, Pornchai Phukpattaranont, Chusak Limsakul, Saara M. Rissanen, Markku
Kankaanpää, Mika P. Tarvainen, Pasi A. Karjalainen, Chiharu Ishii, Adriano O. Andrade,
Alcimar B. Soares, Slawomir J. Nasuto, Peter J. Kyberd, Ryuji Sakakibara, Tomoyuki Uchiyama,
Tatsuya Yamamoto, Fuyuki Tateno, Tomonori Yamanishi, Masahiko Kishi, Yohei Tsuyusaki,
Takeshi Tsujimura, Sho Yamamoto, Kiyotaka Izumi, Gabriela Winkler Favieiro, Alexandre
Balbinot, R.K. Jain, S. Datta, S. Majumder, César Ferreira Amorim, Runer Augusto Marson,
Alcimar Barbosa Soares, Edgard Afonso Lamounier Júnior, Adriano de Oliveira Andrade,
Alexandre Cardoso, Muhammad Zahak Jamal
Published by InTech
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Copyright © 2012 InTech
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Notice
Statements and opinions expressed in the chapters are these of the individual contributors and
not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy
of information contained in the published chapters. The publisher assumes no responsibility for
any damage or injury to persons or property arising out of the use of any materials,
instructions, methods or ideas contained in the book.
Publishing Process Manager Sandra Bakic
Typesetting InTech Prepress, Novi Sad
Cover InTech Design Team
First published October, 2012
Printed in Croatia
A free online edition of this book is available at www.intechopen.com
Additional hard copies can be obtained from orders@intechopen.com
Computational Intelligence in Electromyography Analysis –
A Perspective on Current Applications and Future Challenges, Edited by Ganesh R. Naik
p. cm.
ISBN 978-953-51-0805-4
Contents
Preface IX
Section 1 EMG Modelling 1
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 117
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
VI Contents
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 247
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 307
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
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
.
Electromyography and Kinematic Measurements
in Parkinson’s Disease 221
Saara M. Rissanen, Markku Kankaanpää,
Mika P. Tarvainen and Pasi A. Karjalainen. Thongpanja,
Huosheng Hu, Pornchai Phukpattaranont, Chusak Limsakul, Saara M. Rissanen, Markku
Kankaanpää, Mika P. Tarvainen, Pasi A. Karjalainen, Chiharu
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