**8. References**


<sup>\*</sup> Corresponding Author

Artemiadis P. K. & Kyriakopoulos K. J. (2010). EMG-based control of a robot arm using lowdimensional embeddings, *IEEE Transactions on Robotics*, Vol. 26, No. 2, pp. 393-398.

Computational Intelligence in Electromyography Analysis – 384 A Perspective on Current Applications and Future Challenges

, S. Datta and S. Majumder

*Design of Mechanical System Group/Micro Robotics Laboratory, CSIR-Central Mechanical Engineering Research Institute (CMERI),* 

configurable Micro Manufacturing System (NWP-30)".

Queen's University, Kingston Ontario, Canada.

USA, 14-16April, pp. 241-244.

Vol. 3, No. 2, pp. 1-10.

Corresponding Author

 \*

In future, we will focus on developing well equipped EMG driven micro robotic system where IPMC based micro robotic arm along with multiple IPMC artificial fingers will be used. IPMC based micro robotic arm will be operated through human fore arm movement for lifting and manipulation. Multiple IPMC fingers will be used for robust application like grasping, holding and mimicking of a human hand. Therefore, this new generation of robotic system can be really operated in real world through humans using

The authors are grateful to the Director, Central Mechanical Engineering Research Institute (CMERI), Durgapur, West Bengal, India for providing the permission to publish this book chapter. This work is financially supported by the Council of Scientific and Industrial Research (CSIR), New Delhi, India under eleventh five year plan on "Modular Re-

Ahmad I., Ansari F. & Dey U. K. (2012). A review of EMG recording technique, *International* 

Andreasen D. S., Allen S. K. & Backus D. A. (2005). Exoskeleton with EMG based active assistance for rehabilitation, *Proceedings of the 2005 IEEE 9th International Conference on* 

Andrews J. (2008). Finger movement classification using forearm EMG signals, *MS Thesis,*

Aravinthan P., GopalaKrishnan N., Srinivas P. A. & Vigneswaran N. (2010). Design, development and implementation of neurologically controlled prosthetic limb capable of performing rotational movement, *IEEE International Conference RFID 2010*, Orlando,

Arieta H., Katoh R., Yokoi H. & Wenwei Y. (2006). Development of a multi-DOF electromyography prosthetic system using the adaptive joint mechanism, *ABBI 2006*,

*Journal of Engineering Science and Technology*, Vol. 4, No. 2, pp. 530-539.

*Rehabilitation Robotics,* Chicago, IL, USA, June 28 - July 1, pp. 333-336.

**7. Future direction** 

EMG signals.

R.K. Jain\*

**Author details** 

*Durgapur, West Bengal, India* 

**Acknowledgement** 

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**Chapter 16** 

© 2012 Amorim and Marson, 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 Amorim and Marson, 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.

**Application of Surface Electromyography in** 

Surface electromyography (sEMG) is a generic term for a method of recording electrical muscle activity. Numerous applications for this method have been developed in clinical practice, such as diagnosing neuromuscular diseases, analyzing and determining

sEMG is mainly used in the fields of physiotherapy, dentistry, physical education and

The duration of sEMG activity corresponds to the duration of muscle activation. The amplitude is the level of signal activity and varies with the amount of electrical activity detected in the muscle; it provides information about intensity of muscle activation. The observed sEMG frequency is due to a wide range of factors: muscle composition, characteristics of the action potential of the active muscles fibers, the intramuscular

sEMG signals are also affected by the anatomical and physiological properties of the muscles, neuromuscular control of the peripheral nervous system and the instrumentation

The electronic EMG device amplifies, isolates and filters the electrical signal of muscles that occurs during muscle contraction. This signal must undergo conditioning to be captured [12].

A differential amplifier is, ideally, insensitive to noise and amplifies only the EMG signal, although in practice this is not the case. This situation occurs, first of all, because the noise that reaches the electrodes (inputs) doesn't necessarily have the same magnitude. Moreover, due to technological limitations, differential amplifiers cannot perfectly separate two-signal

abnormalities or disorders and muscular rehabilitation (biofeedback) [3, 12, 27, 28].

**the Dynamics of Human Movement** 

César Ferreira Amorim and Runer Augusto Marson

coordination process and electrode properties [22, 23, 28].

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/52463

**1. Introduction** 

biomechanics [12].

used to collect the signal.

input.
