WAVELET TRANSFORM MOMENTS FOR FEATURE EXTRACTION FROM TEMPORAL SIGNALS

Ignacio Rodriguez Carreño, Marko Vuskovic

Abstract

A new feature extraction method based on five moments applied to three wavelet transform sequences has 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.

References

  1. Christianini N., Shawe-Taylor, 2000. An Introduction to Support Vector Machines. Cambridge Univ. Press.
  2. Du S. and Vuskovic M., 2004. Temporal vs. Spectral approach to Feature Extraction from Prehensile EMG Signals. In IEEE Int. Conf. on Information Reuse and Integration (IEEE IRI-2004), Las Vegas, Nevada.
  3. Englehart K., Hudgins B., Parker P. and Stevenson M., 1998. Time-frequency representation for classification of the transient myoelectric signal. In ICEMBS'98. Proceedings of the 20th Annual International Conference on Engineering in Medicine and Biology Society. ICEMBS Press.
  4. Englehart K., 1998a. Signal Representation for Classification of the Transient Myoelectric Signal. Doctoral Thesis. University of New Brunswick, Fredericton, New Brunswick, Canada.
  5. Englehart K., Hudgins B., Parker P. and Stevenson M., 1999. Improving Myoelectric Signal Classification using Wavelet Packets and Principle Component Analysis. In ICEMBS'99. Proceedings of the 21st Annual International Conference on Engineering in Medicine and Biology Society, ICEMBS Press.
  6. Englehart K., Hudgins B., Parker P., 2001. A Wavelet - Based Continuous Classification Scheme for Multifucntion Myoelectric Control. In IEEE Transactions on Biomedical Engineering, vol. 48, No. 3, pp. 302- 311.
  7. Farry, K. A., Walker I. D., Baraniuk R. G., 1996. Myoelectric Teleoperation of a Complex Robotic Hand. IEEE Trans On Robotic and Automation, Vol. 12, No.5.
  8. Gunn S.R., 1997. Support Vector Machines for Classification and Regression. Technical Report, Image Speech and Intelligent Systems Research Group, University of Southampton.
  9. Hannaford B. and Lehman S., 1986. Short Time Fourier Analysis of the Electromyogram: Fast Movements and Constant Contraction. In IEEE Transactions On Biomedical Engineering. BME-33,
  10. Han-Pan Huang H-P, Chen C_Y., 1999. Development of a Myoelectric Discrimination System for Multi-Degree Prosthetic Hand. Proc. of the 1999 International Conference on Robotics and Automation, Detroit, May pp. 2392-2397.
  11. Hudgins, P. Parker and R.N. Scott, 1991. A Neural Network Classifier for Multifunctional Myoelectric Control. Annual Int. Conf. Of the EMBS, Vol. 13, No. 3, pp. 1454-1455.
  12. Hudgins B., Parker P. and Scott R. N., 1993. A New Strategy for Multifunctional Myoelectric Control. In IEEE Transactions on Biomedical Engineering, vol. 40, No. 1, pp. 82-94.
  13. Saito N. and Coifman R. R., 1995. Local Discriminant Basis and their applications. J. Math. Imag. Vis., Vol. 5, no 4, pp. 337-358.
  14. Nishikawa D, Yu W. Yokoi H, and Kakazu Y, 1991. EMG Prosthetic Hand Controller using Real-Time Learning Method. In Proc. of the IEEE Conf. on SMC, Vol. 1, pp. I 153-158.
  15. Carreño I. R., Vuskovic M., 2005. Wavelet-Based Feature Extraction from Prehensile EMG Signals. In 13th NordicBaltic on Biomedical Engineering and Medical Physics (NBC'05 UMEA), Umea, Sweden, 13-17.
  16. Schlesinger, D., 1919. Der Mechanische Aufbau der Kunstlishen Glieder. In Ersatzglieder und Arbeitshilfen, Springer, Berlin.
  17. Seber G.A.F., 1984. Multivariate Observations, John Wiley & Sons, pp 102-117.
  18. Uchida N. U., Hiraiwa A., Sonehara N., Shimohara K., 1992. EMG Pattern Recognition by Neural Networks for Multi Fingers Control. Proc. of the Annual Int. Conf. of the Engineering in Medicine and Biology Society. Vol 14, Paris, pp.1016-1018.
  19. Vapnik V. N., 1998. Statistical Learning Theory. John Wiley & Sons.
  20. Vuskovic M., Pozos A. L., Pozos R, 1995. Classification of Grasp Modes Based on Electromyographic Patterns of Preshaping Motions. Proc. of the Internat. Conference on Systems, Man and Cybernetics. Vancouver, B.C., Canada, pp. 89-95, 1995.
  21. Vuskovic M., Schmit J., Dundon B. Konopka C., 1996. Hierachical Discrimination of Grasp Modes Using Surface EMGs. Proc. of the Internat. IEEE Conference on Robotics and Automation, Minneapolis, Minnesota, April 22-28. 2477-2483.
  22. Yao, Y., 1965. An Approximate Degrees of Freedom Solution to the Multivariate Behrens-Fisher Problem, Biometrica, Vol. 52, 139-147.
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Paper Citation


in Harvard Style

Rodriguez Carreño I. and Vuskovic M. (2005). WAVELET TRANSFORM MOMENTS FOR FEATURE EXTRACTION FROM TEMPORAL SIGNALS . In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO, ISBN 972-8865-31-7, pages 71-78. DOI: 10.5220/0001190100710078


in Bibtex Style

@conference{icinco05,
author={Ignacio Rodriguez Carreño and Marko Vuskovic},
title={WAVELET TRANSFORM MOMENTS FOR FEATURE EXTRACTION FROM TEMPORAL SIGNALS},
booktitle={Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,},
year={2005},
pages={71-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001190100710078},
isbn={972-8865-31-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,
TI - WAVELET TRANSFORM MOMENTS FOR FEATURE EXTRACTION FROM TEMPORAL SIGNALS
SN - 972-8865-31-7
AU - Rodriguez Carreño I.
AU - Vuskovic M.
PY - 2005
SP - 71
EP - 78
DO - 10.5220/0001190100710078