**3. Prosthetic limb developments**

As previously discussed EMG signals provide a non-invasive measure of ongoing muscle activity. Therefore, EMG signals can be potentially used for controlling robotic prosthetic devices. Most prosthetic devices that are currently available usually only have one degreeof-freedom. As a result, these devices provide nowhere near the amount of control as the original limb which they are intending to replace. Through clinical research, it has been shown that amputees and partially paralyzed individuals typically have intact muscles that they can exercise varying degrees of control over. As a result, research is being conducted in regards to utilizing the signals from these intact muscles to control robotic devices with multiple degrees of freedom (Beau 2005). The EMG has been used in two manners in the area of prosthetic limb development. The first approach is for the subject to exert a force with a particular muscle. This force results in a steady-state EMG signal amplitude estimate. A degree of freedom of a robotic limb is then moved in proportion to the EMG amplitude. This described approach is used in the control of a standard prosthetic gripper that has one degree-of-freedom (Beau 2005).

The second manner that EMG signals are used involves discrete actions. When a discrete action is performed, such as the quick movement of the hand or arm, the surface EMG is obtained from various muscle cites. The temporal structure of the transient EMG activity is then analyzed. Upon analyzing the transient EMG activity, various movements can be classified. Hence, EMG signals can be used in the development of advanced prosthetic devices that have various degrees-of-freedom.

## **4. Muscle anatomy**

Agonist-antagonist muscles exist in many human joint. Such human joint is usually activated by many muscles .The following is a summary of the muscles that are responsible for the movement of the arm, wrist, and hand. Abduction of the arm is performed by the deltoid. Human elbow is mainly actuated by two antagonist muscles: biceps and triceps, although it consists of more muscles. Consequently, biceps and a part triceps are biparticular muscles. By adjusting the amount of force generated by these muscles, the elbow angle and impedance can be arbitrary controlled (Kiguchi et al., 2001). Contraction of the biceps brachii flexes the elbow. Contraction of triceps brachii extends the elbow. Most of the muscles that move the forearm and hand originate within the forearm. The extensor carpi radialis produces extension and abduction of the wrist. The flexor carpi ulnaris flexes and adducts the wrist (Elliott,1998).

### **5. Feature parameters**

EMG classification is one of the most difficult pattern recognition problems because there exist large variations in EMG features. Especially, it is difficult to extract useful features from the residual muscle of an amputee. So far, many researches proposed many kinds of EMG feature to classify posture and they showed good performance. However, how to select a feature subset with the Best discrimination ability from those features is still an issue for classifying EMG signals (Huang et al., 2003). The success of any pattern classification system depends almost entirely on the choice of features used to represent the raw signals. It is desirable to use multiple feature parameters for EMG pattern classification since it is very difficult to extract a feature parameter which reflects the unique feature of the measured signals to a motion command perfectly. But the inclusion of an additional feature parameter with a small separability may degrade overall pattern recognition performance. The feature parameters of EMG signal are listed in Table .1 .


**Table 1.** Feature Parameters of EMG signal

The MATLAB code of this action is:

368 MATLAB – A Fundamental Tool for Scientific Computing and Engineering Applications – Volume 1

As previously discussed EMG signals provide a non-invasive measure of ongoing muscle activity. Therefore, EMG signals can be potentially used for controlling robotic prosthetic devices. Most prosthetic devices that are currently available usually only have one degreeof-freedom. As a result, these devices provide nowhere near the amount of control as the original limb which they are intending to replace. Through clinical research, it has been shown that amputees and partially paralyzed individuals typically have intact muscles that they can exercise varying degrees of control over. As a result, research is being conducted in regards to utilizing the signals from these intact muscles to control robotic devices with multiple degrees of freedom (Beau 2005). The EMG has been used in two manners in the area of prosthetic limb development. The first approach is for the subject to exert a force with a particular muscle. This force results in a steady-state EMG signal amplitude estimate. A degree of freedom of a robotic limb is then moved in proportion to the EMG amplitude. This described approach is used in the control of a standard prosthetic gripper that has one

The second manner that EMG signals are used involves discrete actions. When a discrete action is performed, such as the quick movement of the hand or arm, the surface EMG is obtained from various muscle cites. The temporal structure of the transient EMG activity is then analyzed. Upon analyzing the transient EMG activity, various movements can be classified. Hence, EMG signals can be used in the development of advanced prosthetic

Agonist-antagonist muscles exist in many human joint. Such human joint is usually activated by many muscles .The following is a summary of the muscles that are responsible for the movement of the arm, wrist, and hand. Abduction of the arm is performed by the deltoid. Human elbow is mainly actuated by two antagonist muscles: biceps and triceps, although it consists of more muscles. Consequently, biceps and a part triceps are biparticular muscles. By adjusting the amount of force generated by these muscles, the elbow angle and impedance can be arbitrary controlled (Kiguchi et al., 2001). Contraction of the biceps brachii flexes the elbow. Contraction of triceps brachii extends the elbow. Most of the muscles that move the forearm and hand originate within the forearm. The extensor carpi radialis produces extension and abduction of the wrist. The flexor carpi ulnaris flexes and

EMG classification is one of the most difficult pattern recognition problems because there exist large variations in EMG features. Especially, it is difficult to extract useful features from the residual muscle of an amputee. So far, many researches proposed many kinds of EMG feature to classify posture and they showed good performance. However, how to

**3. Prosthetic limb developments** 

degree-of-freedom (Beau 2005).

**4. Muscle anatomy** 

adducts the wrist (Elliott,1998).

**5. Feature parameters** 

devices that have various degrees-of-freedom.

