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**Section 3** 

**EMG Applications:** 

**Hand Gestures and Prosthetics** 


**EMG Applications: Hand Gestures and Prosthetics** 

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**Chapter 13** 

© 2012 Tsujimura et al., 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 Tsujimura et al., 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.

**Hand Sign Classification** 

Additional information is available at the end of the chapter

processes and huge amount of calculations.

http://dx.doi.org/10.5772/51080

**1. Introduction** 

our proposed method.

**Employing Myoelectric Signals of Forearm** 

Electromyogram (EMG) signals are generated in muscles, when the muscles contract and a joint is flexed or extended. EMG signals can be measured from a skin surface with noninvasive electrodes, and they include some information on motions such as muscle torque or joint angles. Hence, it is possible to achieve more intuitive human-machine interface using EMG signals than conventional interfaces such as joysticks, data gloves, motion captures. Various interfaces using EMG signals have been proposed to control robot hands (Graupe et al.; Jacobson et al.; Yoshikawa et al., 2009; Ibe at al.). Some methods for hand motion identification have been reported since the 1990s based on soft-computing approaches, e. g. artificial neural networks (Fukuda et al.; Hudgins et al.), fuzzy logic (Karlik & Tokhi; Chan et al.), support vector machine (Yoshikawa et al., 2007; Oskoei & Huosheng), and so on (Chen et al.; Huang et al.). These approaches have improved accuracy of motion discrimination and the number of discriminated motions. However, they need complicated

The purpose of our study is to design an uncomplicated system to identify finger motion and to develop innovative human-machine interfaces. We began with the investigation of the forearm muscle EMG (Tsujimura et al.; Yamamoto et al.). We supposed that not only finger muscles but forearm ones work when the knuckles display hand signs. For this purpose, an EMG measurement system is constructed first to detect surface EMG signals of a forearm and to convert them to more manageable types of features. We next evaluate the correlation between the forearm EMG signals and finger motions. It discloses the activity pattern of each forearm muscle corresponding to specific hand sign. The identification algorithm of hand signs is then designed based on the optimized criterion of muscle activity. Finally, identification of finger gesture is experimented to demonstrate the effectiveness of

Takeshi Tsujimura, Sho Yamamoto and Kiyotaka Izumi
