Evaluation of the forearm EMG signal features for the control of a prosthetic hand.

The purpose of this research is to select the best features to have a high rate of motion classification for controlling an artificial hand. Here, 19 EMG signal features have been taken into account. Some of the features suggested in this study include combining wavelet transform with other signal processing techniques. An assessment is performed with respect to three points of view: (i) classification of motions, (ii) noise tolerance and (iii) calculation complexity. The energy of wavelet coefficients of EMG signals in nine scales, and the cepstrum coefficients were found to produce the best features in these views.

[1]  S.G. Meek,et al.  Fatigue compensation of the electromyographic signal for prosthetic control and force estimation , 1993, IEEE Transactions on Biomedical Engineering.

[2]  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.

[3]  Carole A. Tucker.  Self-organizing maps for time series analysis of electromyographic data , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[4]  Stéphane Mallat,et al.  Zero-crossings of a wavelet transform , 1991, IEEE Trans. Inf. Theory.

[5]  A. Willsky,et al.  Upper Extremity Limb Function Discrimination Using EMG Signal Analysis , 1983, IEEE Transactions on Biomedical Engineering.

[6]  D Graupe,et al.  Stochastic analysis of myoelectric temporal signatures for multifunctional single-site activation of prostheses and orthoses. , 1985, Journal of biomedical engineering.

[7]  George N. Saridis,et al.  EMG Pattern Analysis and Classification for a Prosthetic Arm , 1982, IEEE Transactions on Biomedical Engineering.

[8]  C. L. Nikias,et al.  Higher-order spectra analysis : a nonlinear signal processing framework , 1993 .

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

[10]  Eric Abel,et al.  Inter-scale local maxima-a new technique for wavelet analysis of EMG signals , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

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

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

[13]  Jin-Shin Lai,et al.  Using time-frequency analysis technique in the classification of surface EMG signals , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  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.

[15]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  P. A. Parker,et al.  Improving myoelectric signal classification using wavelet packets and principal components analysis , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

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

[18]  C.I. Christodoulou,et al.  Unsupervised pattern recognition for the classification of EMG signals , 1999, IEEE Transactions on Biomedical Engineering.

[19]  L Philipson,et al.  The electromyographic signal as a measure of muscular force: a comparison of detection and quantification techniques. , 1988, Electromyography and clinical neurophysiology.

[20]  Arnon D. Cohen,et al.  Biomedical Signal Processing , 1986 .

[21]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .