**13. Conclusions**

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 performance indices over all movements.

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 of recognition with noisy signal having lower SNR for the neural network and different values of k. It can be found that the K-NN method with the value of k=15 achieves better performance than neural network method and K-NN with the other values of k. The reason of successful of K-NN algorithm that, the input signal may be similar to other frames of EMG signal due to the effect of the noise, therefore ,it is necessary to check the input EMG signal with more frames of EMG signal to give good recognition result .

The possible reason for the poor results of ANN may be due to the simple decision function realized by this method. EMG signal has variation with time therefore necessary to check the input signal with more frame for each muscle as shown in K-NN which provides more accuracy in the recognition. The choice multiple features provide more information about the input signal, failer one of these features can be repaired by the other features.

This chapter also presents the simulation of human arm motion in virtual reality to test the algorithm of EMG recognition. It can be concluded that, The Virtual Reality is useful to test the viability of designs before the implementation phase on a virtual reality prototype. It found that, MATLAB a convenient platform for development of computational algorithms, and with the visualization functions of MATLAB Ver.R2009a a reasonable amount of visualization techniques are available.
