A Supervised Feature-Projection-Based Real-Time EMG Pattern Recognition for Multifunction Myoelectric Hand Control

Electromyographic (EMG) pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study was to develop an efficient feature- projection method for EMG pattern recognition. To this end, a linear supervised feature projection is proposed that utilizes a linear discriminant analysis (LDA). First, a wavelet packet transform (WPT) is performed to extract a feature vector from four-channel EMG signals. To dimensionally reduce and cluster the WPT features, an LDA, then, incorporates class information into the learning procedure, and identifies a linear matrix to maximize the class separability for the projected features. Finally, a multilayer perceptron classifies the LDA-reduced features into nine hand motions. To evaluate the performance of the LDA for WPT features, the LDA is compared with three other feature-projection methods. From a visualization and quantitative comparison, it is shown that the LDA produces a better performance for the class separability, plus the LDA-projected features improve the classification accuracy with a short processing time. A real-time pattern-recognition system is then implemented for a multifunction myoelectric hand. Experiments show that the proposed method achieves a 97.4% recognition accuracy, and all processes, including the generation of control commands for the myoelectric hand, are completed within 97 ms. Consequently, these results confirm that the proposed method is applicable to real-time EMG pattern recognition for multifunction myoelectric hand control.

[1]  Ronald R. Coifman,et al.  Local discriminant bases and their applications , 1995, Journal of Mathematical Imaging and Vision.

[2]  Katsunori Shimohara,et al.  EMG pattern recognition by neural networks for prosthetic fingers control , 1992 .

[3]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.

[4]  M. Osman Tokhi,et al.  A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis , 2003, IEEE Transactions on Biomedical Engineering.

[5]  S H Park,et al.  EMG pattern recognition based on artificial intelligence techniques. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  Kevin B. Englehart,et al.  A robust, real-time control scheme for multifunction myoelectric control , 2003, IEEE Transactions on Biomedical Engineering.

[7]  David G. Lowe,et al.  Optimized Feature Extraction and the Bayes Decision in Feed-Forward Classifier Networks , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  R.N. Scott,et al.  A new strategy for multifunction myoelectric control , 1993, IEEE Transactions on Biomedical Engineering.

[9]  Richard T. Johnson,et al.  Development of the Utah Artificial Arm , 1982, IEEE Transactions on Biomedical Engineering.

[10]  Hong Liu,et al.  DLR-Hand II: next generation of a dextrous robot hand , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[11]  C M Light,et al.  Development of a lightweight and adaptable multiple-axis hand prosthesis. , 2000, Medical engineering & physics.

[12]  Han-Pang Huang,et al.  EMG classification for prehensile postures using cascaded architecture of neural networks with self-organizing maps , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[13]  Peter J. Kyberd,et al.  MARCUS: a two degree of freedom hand prosthesis with hierarchical grip control , 1995 .

[14]  Adrian D. C. Chan,et al.  Continuous myoelectric control for powered prostheses using hidden Markov models , 2005, IEEE Transactions on Biomedical Engineering.

[15]  A.D.C. Chan,et al.  Optimized Gaussian mixture models for upper limb motion classification , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Toshio Tsuji,et al.  A human-assisting manipulator teleoperated by EMG signals and arm motions , 2003, IEEE Trans. Robotics Autom..

[17]  Kevin B. Englehart,et al.  A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.

[18]  F. K. Lam,et al.  Fuzzy EMG classification for prosthesis control. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[19]  K. Englehart,et al.  Classification of the myoelectric signal using time-frequency based representations. , 1999, Medical engineering & physics.

[20]  Wenwei Yu,et al.  EMG prosthetic hand controller discriminating ten motions using real-time learning method , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[21]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[23]  Vwani P. Roychowdhury,et al.  On self-organizing algorithms and networks for class-separability features , 1997, IEEE Trans. Neural Networks.