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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.

has been cited by the following article:

• TITLE: Discriminant Analysis for Human Arm Motion Prediction and Classifying

  AUTHORS: Mohammed Z. Al-Faiz, Sarmad H. Ahmed

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

  JOURNAL NAME: Intelligent Control and Automation, Vol.4 No.1, February 6, 2013

  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.