**7. References**

Chan, F. H., Lam, Y. Y., Zhang, Y., & Parker, P. A. (2000). Fuzzy EMG classification for prosthesis control, *IEEE Transactions on Rehabilitation Engineering*, Vol. 8, No. 3, (2000), pp. 305–311

Chen, X., Zhang, X., Zhao, Z., Yang, J., Lantz, V., & Wang, K. (2007). Multiple Hand Gesture Recognition based on Surface EMG Signal, *The 1st International Conference on Bioinformatics and Biomedical Engineering 2007*,(2007), pp.506-509

Computational Intelligence in Electromyography Analysis – 334 A Perspective on Current Applications and Future Challenges

**6. Future directions** 

vague myoelectric information.

other perceptual devices.

**Author details** 

Takeshi Tsujimura

Sho Yamamoto

Kiyotaka Izumi

**7. References** 

pp. 305–311

system for handicapped persons.

*Department of Mechanical Engineering, Saga University, Japan* 

*Department of Mechanical Engineering, Saga University, Japan* 

*Department of Mechanical Engineering, Saga University, Japan* 

usefulness.

machine.

based on the activity of forearm muscles instead of finger muscles.

estimation system at last. Thus, the method to estimate hand signs has been established

Our study was just started with myoelectrical analysis on the forearm muscles and finger motion. Proposed technique was applied to the hand game called "rock-paper-scissors." But it was configured so as to verify the validity of the method rather than to demonstrate its

We are planning to improve the technique to human-robot interface system. The advanced input device for computers is one of the applications, which is more intuitive than the dataglove. Such versatile system is neccesary to distinguish many finger motions based on

This paper evaluated the myoelectric signals processed simply by statistic evaluation for noise reduction or feature extraction. Higher performance may be expected by introducing some meta-heuristic method or intelligent method, e. g. neural networks, or suppot vector

The muscle activity was determined on the basis of dualistic taxology according to the criterion established beforehand in this paper. It is necessary to determine the criterion

Practical systems will be realized by integrated measurement method combined with the

The proposed technique will be improved not only to the engineering applications but the medical ones, such as the bio-feedback system in rehabilitation or the phyisical support

Chan, F. H., Lam, Y. Y., Zhang, Y., & Parker, P. A. (2000). Fuzzy EMG classification for prosthesis control, *IEEE Transactions on Rehabilitation Engineering*, Vol. 8, No. 3, (2000),

adaptively to the measument conditions to apply to realtime robotic systems.

	- Yoshikawa, M., Mikawa, M., & Tanaka, K. (2009). Real-Time Hand Motion Classification Using EMG Signals with Support Vector Machines, *The IEICE Transactions on information and systems (Japanese edition)*, Vol.J92-D, No.1, (2009), pp.93-103

**Chapter 14** 

© 2012 Winkler Favieiro and Balbinot, 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

distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Winkler Favieiro and Balbinot, 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,

properly cited.

**Proposal of a Neuro Fuzzy System** 

Gabriela Winkler Favieiro and Alexandre Balbinot

Additional information is available at the end of the chapter

**Hand-Arm Segment** 

http://dx.doi.org/10.5772/48793

**1. Introduction** 

**for Myoelectric Signal Analysis from** 

Many with disabilities have some difficulties in integrating into society due to impossibility or to restriction in performing simple tasks of day-to-day. This situation is gradually changing by virtue of technological development in the biomedical instrumentation area in respect of human rehabilitation and especially with in the development of assistive technology managed by computational intelligence (computing algorithms and learning machines using techniques as fuzzy logic, artificial neural networks, genetic algorithms, support vector machines, among others). Scientific researches in this area are allowing the development of several mechanisms to improve the life quality of people with special needs, making them more independent and more likely to real social and economical integration.

It's possible to cite, for example, research related to robotic prosthesis. The development of system managed by myoelectric signals (MES) with the intention to mimic the human arm movement, is far from perfect, making the subject of many researches (Ajiboye & Weir, 2005; Chan et al., 2000; Englehart & Hudgins, 2003; Favieiro & Balbinot, 2011; Favieiro et al., 2011; Hincapie & Kirsch, 2009; Hudgins et al., 1991; Hudgins et al., 1994; Jacobsen et al., 1982; Katutoshi et al., 1992; Khushaba et al., 2010; Momen et al., 2007; Park & Meek, 1995). These researches are mainly being conducted in able-bodies subjects to verify the feasibility and performance of different algorithms for pattern recognition using EMG signals from the forearm muscles. In these studies are usually employed a high number of electrode pairs, ranging from 4 to 12. Using classification patterns techniques such as LDA, fuzzy logic, among others, was found high accuracies (>90%) for the classification of different moves ranging from four to ten. Develop a robotic prosthesis as similar as possible to the human arm is not a simple task. There are great difficulties both in the area of distinguish the
