Discriminant Analysis for Human Arm Motion Prediction and Classifying


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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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.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] 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.
[2] 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.
[3] 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. doi:10.1109/TRA.2003.808873
[4] 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.
[5] 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
[6] K. Fukunaga, “Introduction to Statistical Pattern Recognition,” 2nd Edition, Academic Press Professional, Inc., San Diego, 1990.
[7] 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. doi:10.1109/TIT.1967.1053964
[8] 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. doi:10.1109/TSMCB.2008.2011816
[9] 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

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