Discriminant Analysis for Human Arm Motion Prediction and Classifying

Mohammed Z. Al-Faiz, Sarmad H. Ahmed

Department of Computer Engineering, Al-Nahrain University, Baghdad, Iraq.
[e-mail].

DOI: 10.4236/ica.2013.41004                

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.

Keywords

Linear Discriminant Analysis (LDA); k-Nearest Neighbor (k-NN)

Conflicts of Interest

The authors declare no conflicts of interest.

References

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