TITLE:
Discriminant Analysis for Human Arm Motion Prediction and Classifying
AUTHORS:
Mohammed Z. Al-Faiz, Sarmad H. Ahmed
KEYWORDS:
Linear Discriminant Analysis (LDA); k-Nearest Neighbor (k-NN)
JOURNAL NAME:
Intelligent Control and Automation,
Vol.4 No.1,
February
6,
2013
ABSTRACT:
The EMG signal which is generated by the muscles activity diffuses to the skin surface of human body. This paper presents a pattern recognition system based on Linear Discriminant Analysis (LDA) algorithm for the classification of upper arm motions; where this algorithm was mainly used in face recognition and voice recognition. Also a comparison between the Linear Discriminant Analysis (LDA) and k-Nearest Neighbor (k-NN) algorithm is made for the classification of upper arm motions. The obtained results demonstrate superior performance of LDA to k-NN. The classification results give very accurate classification with very small classification errors. This paper is organized as follows: Muscle Anatomy, Data Classification Methods, Theory of Linear Discriminant Analysis, k-Nearest Neighbor (kNN) Algorithm, Modeling of EMG Pattern Recognition, EMG Data Generator, Electromyography Feature Extraction, Implemented System Results and Discussions, and finally, Conclusions. The proposed structure is simulated using MATLAB.