On automatic identification of upper-limb movements using small-sized training sets of EMG signals.

We evaluate the performance of a variety of neural and fuzzy networks for discrimination among three planar arm-pointing movements by means of electromyographic (EMG) signals, when learning is based on small-sized training sets. The aim of this work is to underline the importance that the sparse data problem has in designing pattern classifiers with good generalisation properties. The results indicate that one of the proposed fuzzy networks is more robust than the other classifiers when working with small training sets.

[1]  S Micera,et al.  A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques. , 1999, Medical engineering & physics.

[2]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[3]  T. Kuo,et al.  The application of cepstral coefficients and maximum likelihood method in EMG pattern recognition. , 1995, IEEE transactions on bio-medical engineering.

[4]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[5]  T S Kuo,et al.  Real-time implementation of electromyogram pattern recognition as a control command of man-machine interface. , 1996, Medical engineering & physics.

[6]  Shigeo Abe,et al.  A method for fuzzy rules extraction directly from numerical data and its application to pattern classification , 1995, IEEE Trans. Fuzzy Syst..

[7]  Daniel Graupe,et al.  Artificial neural network control of FES in paraplegics for patient responsive ambulation , 1995 .

[8]  R.N. Scott,et al.  The application of neural networks to myoelectric signal analysis: a preliminary study , 1990, IEEE Transactions on Biomedical Engineering.

[9]  Daniel Graupe,et al.  Functional Separation of EMG Signals via ARMA Identification Methods for Prosthesis Control Purposes , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Chi Hau Chen,et al.  Fuzzy logic and neural network handbook , 1996 .

[11]  Bruce C. Wheeler,et al.  EMG feature evaluation for movement control of upper extremity prostheses , 1995 .

[12]  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.

[13]  E W Abel,et al.  Neural network analysis of the EMG interference pattern. , 1996, Medical engineering & physics.

[14]  P E Patterson,et al.  Identification of lower arm motions using the EMG signals of shoulder muscles. , 1994, Medical engineering & physics.