A k-nearest neighbor based algorithm for human arm movements recognition using EMG signals

In a human-robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control human-assisting manipulators. The electromyography (EMG) signals can be used as a control source of artificial arm after it has been processed. The objective of this work is to achieve better classification with multiple parameters using K-Nearest Neighbor for different movements of a prosthetic arm. A K- Nearest Neighbor (K-NN) rule is one of the simplest and the most important methods in pattern recognition. The proposed structure is simulated using MATLAB Ver. R2009a, and satisfied results are obtained by comparing with conventional method of recognition using Artificial Neural Network(ANN), that explains the ability of the proposed structure to recognize the movements of human arm based EMG signals. Results show the proposed technique achieved a uniformly good performance with respect to ANN in term of time which is important in recognition systems, better accuracy in recognition when applied to lower SNR signal.