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 Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2012 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. 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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