**1. Introduction**

Computational Intelligence in Electromyography Analysis – 362 A Perspective on Current Applications and Future Challenges

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The medical, rehabilitation and bio-mimetic technology demands human actuated devices which can support in the daily life activities such as functional assistance or functional substitution of human organs. These devices can be used in the form of prosthetic, skeletal and artificial muscles devices (Andreasen et al., 2005; Bitzer & Smagt, 2006; DoNascimento et al., 2008). However, we still have some difficulties in the practical use of these devices. The major challenges to overcome are the acquisition of the user's intention from his or her bionic signals and to provide with an appropriate control signal for the device. Also, we need to consider the mechanical design issues such as lightweight and small size with flexible behavior etc (Arieta et al., 2006; Shenoy et al., 2008). For the bionic signals, the electromyography (EMG) signal can be used to control these devices, which reflect the muscles motion, and can be acquired from the body surface. We are familiar with the fact that ionic polymer metal composite (IPMC) has tremendous potential as an artificial muscle. This can be stimulated by supplying a small voltage of ±3V and shows evidence of a large bending behavior (Shahinpoor & Kim, 2001; 2002; 2004; Bar-Cohen, 2002). In place of the supply voltage from external source for actuating an IPMC, EMG signal can be used where EMG electrodes show a reliable approach to extract voltage signal from body (Jain et al. 2010a; 2010b; 2011). Using this voltage signal via EMG sensor, IPMC can illustrate the biomimetic behavior through the movement of human muscles. Therefore, an IPMC is used as an artificial muscle finger for the bio-mimetic/micro robot.

The main objective of this chapter is to discuss the design and control of an IPMC based artificial muscle finger where this finger is actuated by EMG signal via movement of human finger. The movement is sensed by EMG sensor which provides signal for actuating the IPMC. When designing IPMC artificial muscle finger based micro gripper for handling the light weight components in an assembly, IPMC bending behaviour is utilized to hold the

© 2012 Jain 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 Jain 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.

object. During holding the object, stable EMG signal is required. For this purpose, stable EMG signal is sent through proportional–integral–derivative (PID) controller to the system. Experimentally, it is found that IPMC based artificial muscle finger achieves similar movement like human index finger. This IPMC based artificial muscle finger attains deflection upto 12 mm. By developing a prototype of IPMC artificial muscle finger based micro gripper, it is demonstrated that EMG driven system like IPMC artificial muscle finger based micro gripper can be applicable in handling of light weight components. The major advantages of such system are that IPMC based artificial muscle finger tip shows the compliant behavior and consumes less energy for actuation. Therefore, EMG driven system shows enough potential to substitute for conventional mechanism in micro manipulation and rehabilitation technology.

Design and Control of an EMG Driven IPMC Based Artificial Muscle Finger 365

polymers such as polytetrafluoroethylene (PTFE), polyether ether ketone (PEEK) and low density metals such as titanium. Lau (2009) have carried out research work on a design and development of an intelligent prosthetic hand based on hybrid actuation through DC motor & SMA wires. These are controlled by myoelectric signal. Two novel features are introduced in the new prosthetic hand. Firstly, its hybrid actuation mechanism has the advantage of increasing the active degrees of freedom and secondly, using only two myoelectric sensors, has the potential for controlling more than three patterns of fingers movements. Pittaccio & Viscuso (2011) have developed a SMA wire device for the rehabilitation of the ankle joint where active orthosis powered by two rotary actuators like, NiTi wire are used to obtain ankle dorsiflexion and EMG signal is used to control the orthosis and trigger activation from muscle. Stirling et al. (2011) have shown the potential of SMA wire for an active, soft orthotic in the knee where NiTi based SMA wires is also used. A prototype is tested on a suspended, robotic leg to simulate the swing phase of a typical gait. Thayer & Priya (2011) have designed a biomimetic dexterous humanoid hand where the dexterity of the DART hand have been measured by quantifying functionality and typing speed on a standard keyboard. The hand consists of 16 servo motors dedicated to finger motion and three motors

Some of the researchers have focused on the design of a biomechatronic robotic hand using EMG. Cheron et al. (1996) have found the relationship between EMG and the arm kinematics through dynamic recurrent neural networks (DRNN) method whereas Hudgins et al. (1997) have focused on a new control scheme, based on the recognition of naturally myoelectric signal patterns, transfers the burden of multifunction myoelectric control from the amputee to the control system. Rosen et al. (2001) have developed a myosignal-based exoskeleton system. This is implemented in an elbow joint, naturally controlled by the human. The human–machine interface is set at the neuromuscular level, by using the neuromuscular signal (EMG) as the primary command signal for the exoskeleton system. The EMG signals along with the joint kinematics are fed into a myoprocessor (Hill-based muscle model) which in turn predicts the muscle moments on the elbow joint. Banks (2001) has given remarkable effort towards design and control of an anthropomorphic robotic finger with multi-point tactile sensation whereas Light et al. (2002) have emphasized on intelligent multifunction myoelectric control of hand prostheses. Peleg et al. (2002) have extracted multiple features via EMG signal from hand amputees which is selected by help of a genetic algorithm. Fukuda et al. (2003) have developed a prosthetic hand where humanassisting manipulator system based on the EMG signals is utilized. Wheeler (2003) has presented a neuro-electric interface method for virtual device control. The sampled EMG data is taken from forearm and then is fed into pattern recognition software that has been trained to distinguish gestures from a given gesture set. Krysztoforski et al. (2004) have given remarkable effort towards recognition of palm finger movements on the basis of EMG

Crawford et al. (2005) have used EMG signals for classifying in real-time with an extremely high degree of accuracy in a robotic arm-and-gripper. A linear support vector machines (SVM) based classifier and a sparse feature representation of the EMG signal are used.

for wrist motion where some of joints are activated through SMA wires.

signals with the application of wavelets.

This chapter is organized as follows: Section 2 describes the prior research related to EMG applications in robotics and bio-mimetics. Section 3.1 explains the basic design of IPMC artificial muscle finger based micro gripper which is driven by EMG signal. The basic tendon of index finger is studied in section 3.2 where muscles are identified for actuation of IPMC based artificial muscle finger. In section 3.3, a model for controlling the EMG signal is highlighted. Different types of control system are implemented for achieving stable data from EMG signal via index finger which is sent to IPMC based artificial muscle finger. Section 4 discusses experimental testing setup for activation of IPMC based artificial muscle finger by human finger through EMG. In section 5, the results are discussed and the conclusions are drawn in section 6. The future work is recommended in section 7.
