**10. Human arm movements recognition based on k-nearest neighbor algorithm**

The discrimination of the EMG signal into the correct class of movement is a fundamental element of the system. The precision of a classifier lies on its capability to give the correct answers in spite of some inaccuracies that may occur during the process of detection of the EMG signal.

To improve precision of a classifier with decrease of the training time, a recognition system based K-NN algorithm is used. The success of any pattern classification system depends almost entirely on the choice of features used to represent the raw signals. In the proposed system multiple feature parameters for EMG pattern classification are used since it is very difficult to extract a feature parameter which reflects the unique feature of the measured signals to a motion command perfectly.

Five kinds of arm motion are recognized: Abduction of the arm, flexion the elbow, extension the elbow, extension and abduction of the wrist and flexes and adducts the wrist .These motions are produced by contraction of five muscles. Therefore ,if the EMG signal of muscle is recognized then the specified motion of this muscle is recognized. The proposed method is outlined in Fig. 9 and the stages of proposed system are discussed below:

**Figure 9.** Structure of the Recognition system based on K-NN classifier
