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Discriminant Analysis for Human Arm Motion Prediction and Classifying

Journal: Intelligent Control and Automation
ISSN 2153-0653

Volume: 04;
Issue: 01;
Start page: 26;
Date: 2013;
Original page

Keywords: Linear Discriminant Analysis (LDA) | k-Nearest Neighbor (k-NN)

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.
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