WAVELET ENERGIES AS A FEATURE AND THEIR IMPACT ON CLASSIFYING MOVEMENTS BASED ON sEMG

Dimitrios S. Barbakos, Nikolaos Strimpakos, Stavros A. Karkanis

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References

  1. [1] A. Hiralwa, N. Uchida and K. Shimohara, “EMG Pattern Recognition by Neural Networks for Prosthetic Fingers Control”, Annual Review in Automatic Programming, Vol. 17, 1992, pp. 7379.
  2. [2] Latwesen, P.E. Patterson, ‘Identification of lower arm motions using the EMG signals of shoulder muscles”, Medical Engineering & Physics, Vol.16 (2), Mar. 1994, pp. 113-121.
  3. [3] Al-Timemy A.H., Bugmann G., Escudero J., Outram N., “Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography”, IEEE Journal of 172 Biomedical and Health Informatics, vol.17(3), May 2013, pp. 608-618.
  4. [4] Kiatpanichagij K., Afzulpurkar N., “Use of supervised discretization with PCA in wavelet packet transformation-based surface electromyogram classification”, Biomedical Signal Processing and Control, Vol.4(2), Apr. 2009, pp. 127-138.
  5. [5] C. Christodoulou and C. S. Pattichis, “A new technique for the classification and decomposition of EMG signals,” in Proc. 1995 IEEE Int. Conf. Neural Networks, New York, 1995(5), pp. 2303–2308.
  6. [6] Y. H. Huang, K. Englehart, B. S. Hudgins, and A. D. C. Chan, “A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses”, IEEE Trans. Biomed. Eng., vol. 52(11), 2005, pp. 1801–1811.
  7. [7] A. D. C. Chan and K. Englehart, “Continuous myoelectric control for powered prostheses using hidden Markov models”, IEEE Trans. Biomed. Eng., vol. 52(1), Jan. 2005, pp. 121–124.
  8. [8] F. H. Chan, Y.-S. Yang, F. K. Lam, Y.-T. Zhang, and P. A. Parker, “Fuzzy EMG classification for prosthesis control”, IEEE Trans. Rehabil. Eng., vol. 8(3), Sep. 2000, pp. 305–311.
  9. [9] Phinyomark A., Limsakul C., and Phukpattaranont P., “A Novel Feature Extraction for Robust EMG Pattern Recognition”, Journal of Computing, vol. 1(1), Dec. 2009, pp. 71-80.
  10. [10] Staudenmann D., Kingma I., Stegeman D.F, van Dieën J. H., ‘Towards optimal multi-channel EMG electrode configurations in muscle force estimation: a high density EMG study”, Journal of Electromyography and Kinesiology, Volume 15(1) , Feb. 2005, pp. 1-11.
  11. [11] Staudenmann D., Roeleveld K., Stegeman D.F., van Dieënemail J.H., “Methodological aspects of sEMG recordings for force estimation – A tutorial and review”, Journal of Electromyography and Kinesiology, Volume 20(3) , June 2010, pp. 375-387.
  12. [12] Atzori M., Gijsberts A., Heynen S., Mittaz Hager A.-G., Deriaz O., Van der Smagt P., Castellini C., Caputo B., and Müller H. , “Building the NinaPro Database: a Resource for the Biorobotics Community” IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), June 2012, pp. 12581265.
  13. [13] P. Yang , Q. Li, “Wavelet transform-based feature extraction for ultrasonic flaw signal classification”, Neural Computing & Applications, Volume 24(3-4), pp. 817-826
  14. [14] Y. Meyer, Wavelets: Algorithms and Application SIAM, Philadelphia, 1993.

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