**3.1. Basic design of IPMC artificial muscle finger based micro gripper using EMG**

For designing an IPMC artificial muscle finger based micro gripper using EMG, an IPMC strip (Size 40 mm × 10 mm× 0.2 mm) that imitates human finger movement, is assumed to be artificial muscle finger. This is fixed with holder and another plastic based finger of similar size is made for supporting the micro object as shown in Fig. 1. When human index finger moves up and down, it creates potential difference by its movements. This potential difference is transferred through EMG electrodes into the artificial muscle finger so that this finger is able to move accordingly and hold the object. The main function of EMG electrode is to detect the voltage from human muscles since human muscles generate few millivoltages when they are contracting or expanding during movement. For transferring this voltage signal to actuate the artificial muscle finger, it needs the amplification setup which is discussed in section 4. Therefore, IPMC based artificial muscle finger is activated using EMG and it allows holding an object for micro assembly operation.

Computational Intelligence in Electromyography Analysis – 368 A Perspective on Current Applications and Future Challenges

from EMG data captured from a patient.

artificial muscle finger for micro gripper in this chapter.

**signal** 

**EMG** 

Recently, Ahmad et al. (2012) have presented a review report on different techniques of EMG data recording where condition of an ideal pre-amplifier, signal conditioning and its amplification are discussed. Sun et al. (2012) have conducted an isokinetic exercise to realize the characteristics of femoral muscles in human knee movement through EMG where a mechanical model of muscle for human knee movement is established. Qi et al. (2012) have developed algorithms for muscle-fatigue detection and muscle-recruitment patterns in routine wheel chair propulsion scenarios, e.g., daily practice where for analysis purpose two speeds of muscular behavior are chosen. Gandole (2012) has developed an artificial intelligent model using focused time lagged recurrent neural network (FTLRNN) method with a single hidden layer. FTLRNN method reduces noise intelligently from the EMG signal. Chan et al. (2012) have developed an assessment platform for upper limb myoelectric prosthetic devices using EMG. The assessment platform consists of an acquisition module, a signal capture module, a programmable signal generation module and an activation & measurement module. The platform is designed to create a sequence of activation signals

An EAP actuator based design for IPMC fingers have been discussed by Biddiss & Chau (2006). This shows the potential of electroactive polymeric sensors within an operating range of voltage (±3V) whereas Kottke et al. (2007) have reported on how to stimulate and activate a non-biological muscle such as an IPMC. Lee et al. (2006, 2007) have also demonstrated the potential of an IPMC actuating system with a bio-mimetic function using EMG signals. A mean absolute method is used for achieving the filtered EMG signal. Aravinthan et al. (2010) have designed a multiple axis prosthetic hand using IPMC. EMG signal through programmable interface controller (PIC) is sent to the IPMC prosthetic material to perform the required actions. By doing experiments, the potential of prosthetic hand using IPMC is shown. After that, we have also demonstrated actuation of IPMC through EMG via forearm muscles where potential of IPMC based micro robotic arm has been shown for lifting the object (Jain et al., 2010a; 2010b; 2011; 2012). For further application of EMG driven system, we are discussing detailed analysis of EMG signal control point view of IPMC based

**3. Design and control of IPMC based artificial finger driven by EMG** 

**3.1. Basic design of IPMC artificial muscle finger based micro gripper using** 

For designing an IPMC artificial muscle finger based micro gripper using EMG, an IPMC strip (Size 40 mm × 10 mm× 0.2 mm) that imitates human finger movement, is assumed to be artificial muscle finger. This is fixed with holder and another plastic based finger of similar size is made for supporting the micro object as shown in Fig. 1. When human index finger moves up and down, it creates potential difference by its movements. This potential difference is transferred through EMG electrodes into the artificial muscle finger so that this finger is able to move accordingly and hold the object. The main function of EMG electrode

**Figure 1.** Schematic diagram of IPMC artificial muscle finger based micro gripper driven by EMG

**Figure 2.** Schematic diagram of the actuation mechanism of IPMC (Chen et al., 2011)

During development of an EMG driven IPMC based artificial muscle finger, a typical IPMC strip (Procured custom made from Environmental Robots Inc., USA) is used which has a thin (approximately 200 μm) perfluorinated ion exchange base polymer membrane (Nafion-117) with metal electrodes of platinum (5–10 μm) fused on either side. As a part of the manufacturing process, this base polymer is further chemically coated with metal ions that comprise the metallic composites. It responds in wet/dry condition. An IPMC is usually kept in a hydrated state to ensure proper dynamic operation. When the material is hydrated, the cations will diffuse toward an electrode on the material surface under an applied electric field. Inside the polymer structure, anions are interconnected as clusters providing channels for the cations to flow towards the electrode (Chen et al., 2011). This motion of ions causes the structure to bend toward the anode as shown in Fig. 2. An applied electric field affects the cation distribution within the membrane, forcing the cations to migrate towards the cathode. This change in the cation distribution produces two thin layers, one near the anode and another near the cathode boundaries. The potential is generated by changing the potential electric field on cluster of ionic strips that provides the actuation of the strip.

Design and Control of an EMG Driven IPMC Based Artificial Muscle Finger 371

*V t V sin ft in ino* (1)

(2)

( ) (2 ) =

Where, *Vino* is amplitude of EMG voltage (±0.0012 V); *f* is frequency of EMG signal (95 Hz).

<sup>=</sup> <sup>+</sup><sup>2</sup> 2.96 ( ) 3.51e5

Using these parameters, the circuit for filtered EMG signal is designed using MATLAB

In block diagram, the active EMG signal is taken from index finger muscle and uniform noise is considered. The electric potential is first amplified with gain 32 dB and then band pass filter (BPF) is used within specified frequency range (4 to 900 Hz). Using two band stop filters (BSF and BSF1), noise signal (60 Hz) that arises due to AC coupled power is eliminated. The signal is then passed through an amplifier with gain 60 dB. Subsequently, three integrators (Integrator, Integrator1 and Integrator2) are used for achieving better damped signal. The output of EMG signal with sampling time of 10-4 seconds after filtering is shown in Fig. 5.

**Analysis of EMG volatge from index finger muscle**

**Curve Fitted signal EMG voltage signal** 

**<sup>0</sup> <sup>100</sup> <sup>200</sup> <sup>300</sup> <sup>400</sup> <sup>500</sup> <sup>600</sup> <sup>700</sup> <sup>800</sup> <sup>900</sup> <sup>1000</sup> -1.5**

**Number of samples** 

*s*

*V s in*

**Figure 4.** Block diagram of EMG signal behaviour from human index finger

**Figure 5.** Acquired data from finger muscles via EMG signal

**-1 -0.5 0 0.5 1**

**Voltage (V)**

**1.5 x 10-3**

In Laplace domain, EMG input signal is written as

SIMULINK software as shown in Fig. 4.

π
