Discriminant Analysis for Human Arm Motion Prediction and Classifying
- 1 mzalfaiz@ieee.org
- 2 Department of Computer Engineering, Al-Nahrain University, Baghdad, Iraq
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 ( k NN) 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.
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