**Author details**

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

Run GUI of EMG Simulator

Build Data Base from GUI

Select the Tested Noisy EMG signal from GUI

Apply the Recognition Process Based K-NN Algorithm

Show the result on text part of GUI and Run the Virtual Reality to visualize the Motion of Human Arm

**Figure 17.** Block diagram for Human Arm Movement based EMG signal

In this chapter the motion classification simulations are carried out, in order to evaluate classification performance of the human arm movements recognition based on k-Nearest Neighbor algorithm. The simulated data were generated from an EMG signal simulator. Several motions are recognized based on classification of five input EMG signals. In the present study, the accuracy for each participant was simply calculated by averaging the

The results illustrate that the recognition using K-NN presents better results than artificial neural network in term of recognition accuracy as shown Table 2. This table shows the result

**13. Conclusions** 

performance indices over all movements.

Mohammed Z. Al-Faiz *Computer Engineering Department, Al-Nahrain University, Baghdad, Iraq* 

Abbas H. Miry *Electrical Engineering Department, AL-Mustansiriyah University, Baghdad, Iraq* 
