Wavelet transform moments for feature extraction from temporal signals

been proposed and used in classification of prehensile surface EMG patterns. The new method has essentially extended the Englehart’s discrete wavelet transform and wavelet packet transform by introducing more efficient feature reduction method that also offered better generalization. The approaches were empirically evaluated on the same set of signals recorded from two real subjects, and by using the same classifier, which was the Vapnik’s support vector machine.

[1]  B. Hannaford,et al.  Short Time Fourier Analysis of the Electromyogram: Fast Movements and Constant Contraction , 1986, IEEE Transactions on Biomedical Engineering.

[2]  Wenwei Yu,et al.  EMG prosthetic hand controller using real-time learning method , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[3]  Ying Yao,et al.  An approximate degrees of freedom solution to the multivariate Behrens-Fisher problem* , 1965 .

[4]  Katsunori Shimohara,et al.  EMG pattern recognition by neural networks for multi fingers control , 1992, 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  A. Marshall Principles of Economics , .

[6]  Han-Pang Huang,et al.  Development of a myoelectric discrimination system for a multi-degree prosthetic hand , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

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

[8]  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.

[9]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[10]  M. I. Vuskoviv,et al.  Classification of grasp modes based on electromyographic patterns of preshaping motions , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[11]  H. Demsetz,et al.  Production, Information Costs, and Economic Organization , 1975, IEEE Engineering Management Review.

[12]  Marko Vuskovic,et al.  Hierarchical discrimination of grasp modes using surface EMGs , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[13]  P. A. Parker,et al.  A Neural Network Classifier For Multifunction Myoelectric Control , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[14]  Maryhelen Stevenson,et al.  Signal representation for classification of the transient myoelectric signal , 1998 .

[15]  G. Schlesinger Der mechanische Aufbau der künstlichen Glieder , 1919 .

[16]  P. A. Parker,et al.  Time-frequency representation for classification of the transient myoelectric signal , 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).

[17]  Ian D. Walker,et al.  Myoelectric teleoperation of a complex robotic hand , 1996, IEEE Trans. Robotics Autom..

[18]  Sijiang Du,et al.  Temporal vs. spectral approach to feature extraction from prehensile EMG signals , 2004, Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, 2004. IRI 2004..

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

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

[21]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[22]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .