**8. References**


in the Severe Domain. Journal Strength and Conditioning Research.2011;25(9) 2537- 2543.

[4] Andrade, M. M., nascimento, F. A. O. Análise tempo-frequência de sinais eletromiográficos de superfície para a avaliação de fadiga muscular em cicloergômetro. Tese de doutorado, UNB. Brasília. 2006.

Computational Intelligence in Electromyography Analysis – 114 A Perspective on Current Applications and Future Challenges

Leandro Ricardo Altimari, José Luiz Dantas,

*CEFE - State University of Londrina, Brazil* 

Marcelo Bigliassi and Thiago Ferreira Dias Kanthack

*Group of Study and Research in Neuromuscular System and Exercise,* 

*GPNeurom - Laboratory of Electromyography Studies, FEF - State University of Campinas, Brazil* 

We are thankful to everyone of the Laboratory of Telecomunications and DSP (Department of Electrical Engineering/CTU, State University of Londrina) and to Dra. Maria Angelica O. C. Brunetto (Department of Computing/CCE, State University of Londrina) that helped with the development of MatLab routine to process the electromyography data and give us the possibility to understand in different perspectives the same cue. The authors thank still the Fundação Araucária do Paraná, the Fundação de Amparo a Pesquisa do Estado de Săo Paulo (FAPESP), and Conselho Nacional de Desenvolvimento Cientifico e Tecnológico (CNPq) for post-graduate scholarships and supported financially. Finally we say thanks to everybody that meticulously contributed with this work, in your write or review process, and additionally keep in thankful to professor Dr. Ganesh Naik for given us the possibility to be part of this wonderful work, helping others to understand the electromyography in

[1] Medved, V. & Cifrek, M. Kinesiological electromyography. Biomechanics in

[2] Massó, N., Rey, F., Romero, D., GuaL, G., Ccosta, L. & Germám, A. Surface electromyography applications in the sport. Apunts Medicina del I'Esport. 2010;45(165)

[3] Camata, T. V., AltimarI, L. R., Bortolotti, H., Dantas, J. L. et al. Electromyographic Activity and Rate of Muscle Fatigue of the Quadriceps Femoris During Cycling Exercise

*Department of Electrical Engineering, CTU - State University of Londrina, Brazil* 

LD – Lower Dispersion from BIAS UD – Upper Dispersion from BIAS

FI – Fatigue Index PPI – Peak Power Instant

**Author details** 

Taufik Abrão

cyclic activities.

**8. References** 

121-130.

applications.4(7) 2010; 349-366.

RPP – Relative Peak Power RMP – Relative Mean Power

Antonio Carlos de Moraes

**Acknowledgement** 

MF – Median


Computational Intelligence in Electromyography Analysis – 116 A Perspective on Current Applications and Future Challenges

[18] Clancy, E. A., Morin, E. L. & Merletti, R. Sampling, noise-reduction and amplitude estimation issues in surface electromyography. Journal of Electromyography and Kinesiology. 2002;1(12) 1-16.

**Section 2** 

**EMG Analysis and Applications** 


**EMG Analysis and Applications** 

Computational Intelligence in Electromyography Analysis – 116 A Perspective on Current Applications and Future Challenges

Kinesiology. 2002;1(12) 1-16.

Kinesiology. 2003;2(13) 169–180.

Biomechanics. 2009;3(24) 225-235.

[18] Clancy, E. A., Morin, E. L. & Merletti, R. Sampling, noise-reduction and amplitude estimation issues in surface electromyography. Journal of Electromyography and

[19] Mclean, L., Chislett, M., Murphy, M. & Walton, P. The effect of head position, electrode site, movement and smoothing window in the determination of a reliable maximum voluntary activation of the upper trapezius muscle. Journal of Electromyography and

[20] Disselhorst-klug, C., Schmitz-rode, T. & Rau, G. Surface electromyography and muscle force: Limits in sEMG–force relationship and new approaches for applications. Clinical

[21] Vencesbrito, A. M. et al. Kinematic and electromyographic analyses of a karate punch.

[22] Farber, A. J. et al. Electromyographic analysis of forearm muscles in professional and

[23] Hopkins, W. G., et al. Progressive statistics for studies in sports medicine and exercise

Journal of Electromyography and Kinesiology. 2011; 21(6) 1023-1029.

science. Medicine and Science in Sports and Exercise. 2009; 41(1) 3-13.

amateur golfers. American Journal of Sports Medicine. 2009;37(2) 396-401.

**Chapter 6** 

© 2012 Lei and Meng, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Lei and Meng, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Nonlinear Analysis of Surface EMG Signals** 

The aim of this chapter is to answer the essence of SEMG and to explore the potential use of nonlinear analysis as a tool in the clinical and biomechanical applications. The technical tools include nonlinear time series test, time series analysis based on chaos theory,

In Section 2, we discuss the two methods of nonlinear time series test: surrogate data test method and Volterra-Wiener-Korenberg (VWK) model test method. Theoretically, the two methods can detect the nonlinearity of the data indirectly. The surrogate data method is used to analyze the SEMG. The result shows that the SEMG has deterministic nonlinear components. Meanwhile, we introduce the VWK model test method and compare it with the surrogate data method. The nonlinearity of SEMG during muscle fatigue can be detected by

In Section 3, we describe the time series analysis based on chaos theory. The chaos definition and chaotic characteristics are discussed. The embedding theory of the attractor reconstruction is investigated for the dynamical system. From the view of the fractal structure of the chaotic attractor, the correlation dimension is used to test the chaotic characteristics of the SEMG during arm movements. The Largest Lyapunov exponent is also studied. Then, we investigate the influence of measure noise, internal noise and sampling interval on the principal components of chaotic time series. The symplectic principal component analysis is given. We illustrate the feasibility of this method and give the

In Section 4, the self-affine fractal definition and nature are described. The power spectrum and frequency relationship is used to calculate the self-affine fractal dimension of the time

The conclusion and future research are shown in Section 5. Here, it is necessary to note that this chapter is actually the result of many years work. The new methods presented here

series, such as SEMG. Then, the multifractal dimension is given for the SEMG.

Min Lei and Guang Meng

http://dx.doi.org/10.5772/49986

**1. Introduction** 

multifractal analysis.

the VWK.

Additional information is available at the end of the chapter

embedding dimension of the action surface EMG signal.
