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.
Cite this paper
M. Al-Faiz and S. Ahmed, "Discriminant Analysis for Human Arm Motion Prediction and Classifying," Intelligent Control and Automation, Vol. 4 No. 1, 2013, pp. 26-31. doi: 10.4236/ica.2013.41004.
 M. Z. Al-Faiz, A. A. Miry and A. H. Ali, “A k-Nearest Neighbor Based Algorithm for Human Arm Movements Recognition Using EMG Signals,” IEEE Proceedings of the 1st International Conference on Energy, Power and Control (EPC-IQ), Basrah, 30 November-2 December 2010, pp. 159-167.
 R. R. Finley and R. W. Wirta, “Myocoder Studies of Multiple Myocoder Response,” Archives of Physical Medicine and Rehabilitation, Vol. 48, 1967, pp. 599-601.
 O. Fukuda, T. Tsuji, M. Kaneko and A. Otsuka, “A Human-Assisting Manipulator Teleoperated by EMG Signals and Arm Motions,” IEEE Transactions on Robotics and Automation, Vol. 19, No. 2, 2000, pp. 210-222.
 N. Bu, M. Okamoto and T, Tsuji, “A Hybrid Motion Classification Approach for EMG-Based Human-Robot Interfaces Using Bayesian and Neural Networks,” IEEE Transactions on Robot, Vol. 23, No. 3, 2009, pp. 502-511.
 S. Park and S. Lee, “EMG Pattern Recognition Based on Artificial Intelligence Techniques,” IEEE Transactions on Rehabilitation Engineering, Vol. 6, No. 4, 1998, pp. 400-405. doi:10.1109/86.736154
 K. Fukunaga, “Introduction to Statistical Pattern Recognition,” 2nd Edition, Academic Press Professional, Inc., San Diego, 1990.
 T. M. Cover and P. E. Hart, “Nearest Neighbor Pattern Classification,” IEEE Transactions on Information Theory, Vol. IT-13, No. 1, 1967, pp. 21-26.
 A. Cervantes, I. Galvan and P. Isasi, “AMPSO: A New Particle Swarm Method for Nearest Neighborhood Classification,” IEEE Transactions on System, Man and Cybernetics, Vol. 39, No. 5, 2009, pp. 1082-1091.
 A. Hamilton-Wright and D. W. Stashuk, “Physiologically Based Simulation of Clinical EMG Signals,” IEEE Transactions on Biomedical Engineering, Vol. 52, No. 2, 2005, pp. 171-183. doi:10.1109/TBME.2004.840501