**6. Conclusion**

Computational Intelligence in Electromyography Analysis – 382 A Perspective on Current Applications and Future Challenges

= +


δ

respectively given below,

signal sample time (s) ;

range (48-52Hz).

and

(Hz); δ

The EMG voltage *VEMG (t)* and IPMC actuation voltage *VIPMC actuation (t)* equations are

π

Where, *V*0E is average value of EMG voltage (V); *f*0E is EMG frequency range (Hz) ; *t* is

is average value of IPMC actuation voltage (V); *f*0I is IPMC actuation frequency range

range of each signal, the experimental data are taken and solved through MATLAB curve fitting tool. The numerical values are *V*0E= 0.001451±0.0002707 V, *f*0E= 4.7±0.006201 Hz,

data, it is found that EMG frequency range (*f01)* is similar to simulated data and IPMC actuation frequency range is 48.5±0.65 Hz which is in between human muscle frequency

**Figure 20.** IPMC based artificial muscle finger based micro gripper driven by EMG

After these analyses, an IPMC artificial muscle finger based micro gripper is developed which is driven by EMG as shown in Fig. 20 where one IPMC based artificial muscle finger and other plastic based finger are fixed with double sided tape within one holder. The IPMC based artificial muscle finger is connected through copper tape and wire with EMG sensor

0I is phase difference when signal is given to IPMC (rad). For finding the frequency

 δ

 δ0 00 ( ) (2 ), *V t V sin f t IPMCactuation <sup>I</sup> I I* 0 0.1 ≤ ≤*t* (14)

0 00 ( ) (2 ), *V t V sin f t EMG <sup>E</sup> E E* 0 0.1 ≤ ≤*t* (13)

0E is phase difference when signal is taken through EMG (rad); *V*0I

δ

δ0E=

0I= -10.5732±0.6556 rad. From these

= + π

> In order to develop the micro/bio-mimetic robot for micro assembly, the potential of an EMG driven artificial finger is discussed in this chapter. An artificial finger for micro assembly is designed using IPMC where IPMC is used as an active artificial finger for holding the object. An IPMC has several advantages such as actuating through a small voltage (±3 V), light in weight, flexible in nature and does not involve sophisticated controllers for operation. For activating the IPMC based artificial finger, voltage is taken from human index finger through EMG sensor instead of battery source as this is used as a man-machine interface device. Principally, EMG sensor acquires the signal from body during expansion or contraction of muscles. These movements are transferred into an IPMC based artificial muscle finger. For achieving the stable data from EMG, different configurations of control methods are analysed. A PID control system is implemented for attaining the noiseless and stable signal from the user's myoelectric signal. While acquiring the data, a differential amplification technique is applied where data is filtered through a band pass filter and noise is eliminated through three band stop filters. For sending this signal to the IPMC, an algorithm has been developed in Labview software which gives emphasis on following points:


Experimentally, it is demonstrated that IPMC based artificial muscle finger is capable of adopting this voltage from EMG signal and mimics as a human finger. From application point of view, an IPMC artificial finger based micro gripper is developed and its capability is also verified. Through this demonstration, it is proved that IPMC can be activated through EMG signal and is applicable as flexible and compliant finger for holding the object in the fields of micro manipulation. IPMC based artificial muscle could also be a replacement of an electro-mechanical system like electric motors in the application field of rehabilitation technology.
