Have a personal or library account? Click to login
Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification Cover

Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification

Measurement Science Review
Volume 11 (2011): Issue 2 (April 2011)
By: A. Phinyomark,  C. Limsakul and  P. Phukpattaranont  
Open Access
|Jun 2011

References

  1. Canal, M. R. (2010). Comparison of wavelet and short time Fourier transform methods in the analysis of EMG signals. J. Med. Syst., 34 (1), 91-94.10.1007/s10916-008-9219-820192059
    Search in Google Scholar
  2. Oskoei, M. A., Hu, H. (2007). Myoelectric control systems - a survey. Biomed. Signal Process. Control., 2 (4), 275-294.10.1016/j.bspc.2007.07.009
    Search in Google Scholar
  3. Pauk, J. (2008). Different techniques for EMG signal processing. J. Vibroeng., 10 (4), 571-576.
    Search in Google Scholar
  4. Neto, O. P., Marzullo, A. C. D. (2009). Wavelet transform analysis of electromyography Kung Fu strikes data. J. Sports Sci. Med., 8 (CSSI 3), 25-28.
    Search in Google Scholar
  5. Kumar, S., Prasad, N. (2010). Torso muscle EMG profile differences between patients of back pain and control. Clin. Biomech., 25 (2), 103-109.10.1016/j.clinbiomech.2009.10.01319944502
    Search in Google Scholar
  6. Kumar, D. K., Pah, N. D., Bradley, A. (2003). Wavelet analysis of surface electromyography to determine muscle fatigue. IEEE Trans. Neural Syst. Rehabil. Eng., 11 (4), 400-406.10.1109/TNSRE.2003.81990114960116
    Search in Google Scholar
  7. Delis, A. L., Carvalho, J. L. A., Rocha, A. F. D., Ferreira, R. U., Rodrigues, S. S., Borges, G. A. (2009). Estimation of the knee joint angle from surface electromyographic signals for active control of leg prostheses. Physiol. Meas., 30 (9), 931-946.10.1088/0967-3334/30/9/00519661566
    Search in Google Scholar
  8. Hussain, M. S., Reaz, M. B. I., Mohd-Yasin, F., Ibrahimy, M. I. (2009). Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction. Expert Syst., 26 (1), 35-48.10.1111/j.1468-0394.2008.00483.x
    Search in Google Scholar
  9. Wang, G., Wang, Z., Chen, W., Zhuang, J. (2006). Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion. Med. Biol. Eng. Comput., 44 (4), 865-872.10.1007/s11517-006-0100-y16951931
    Search in Google Scholar
  10. Englehart, K., Hudgins, B., Parker, P. A. (2001). A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng., 48 (3), 302-311.10.1109/10.91479311327498
    Search in Google Scholar
  11. Chu, J. U., Moon, I., Mun, M. S. (2006). A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric control. IEEE Trans. Biomed. Eng., 53 (11), 2232-2239.
    Search in Google Scholar
  12. Chu, J. U., Moon, I., Lee, Y. J., Kim, S. K., Mun, M. S. (2007). A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE/ASME Trans. Mechatron., 12 (3), 282-290.10.1109/TMECH.2007.897262
    Search in Google Scholar
  13. Boostani, R., Moradi, M. H. (2003). Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiol. Meas., 24 (2), 309-319.10.1088/0967-3334/24/2/30712812417
    Search in Google Scholar
  14. Khezri, M., Jahed, M. (2011). A neuro-fuzzy inference system for sEMG-based identification of hand motion commands. IEEE Trans. Ind. Electron., 58 (5), 1952-1960.10.1109/TIE.2010.2053334
    Search in Google Scholar
  15. Rafiee, J., Rafiee, M. A., Yavari, F., Schoen, M. P. (2011). Feature extraction of forearm EMG signals for prosthetics. Expert Syst. Appl., 38 (4), 4058-4067.10.1016/j.eswa.2010.09.068
    Search in Google Scholar
  16. Reaz, M. B. I., Hussain, M. S., Mohd-Yasin, F. (2006). EMG analysis using wavelet functions to determine muscle contraction. In 8th International Conference on e-Health Networking, Applications and Services, August 2006. New Delhi, India, 132-134.
    Search in Google Scholar
  17. Mahaphonchaikul, K., Sueaseenak, D., Pintavirooj, C., Sangworasil, M., Tungjitkusolmun, S. (2010). EMG signal feature extraction based on wavelet transform. In 7th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications, May 2010. Chiang Mai, Thailand, 356-360.
    Search in Google Scholar
  18. Naik, G. R., Kumar, D. K., Arjunan, S. P. (2010). Pattern classification of Myo-Electrical signal during different Maximum Voluntary Contractions: A study using BSS techniques. Meas. Sci. Rev., 10 (1), 1-6.10.2478/v10048-010-0001-y
    Search in Google Scholar
  19. Yang, G.-Y., Luo, Z.-Z. (2004). Surface electromyography disposal based on the method of wavelet de-noising and power spectrum. In International Confernece on Intelligent Mechatronics and Automation, August 2004. Chengdu, China, 896-900.
    Search in Google Scholar
  20. Buranachai, C., Thanvarungkul, P., Kanatharanaa, P., Meglinski, I. (2009). Application of wavelet analysis in optical coherence tomography for obscured pattern recognition. Laser Phys. Lett., 6 (12), 892-895.10.1002/lapl.200910089
    Search in Google Scholar
  21. Phinyomark, A., Limsakul, C., Phukpattaranont, P. (2010). Optimal wavelet functions in wavelet denoising for multifunction myoelectric control. ECTI Transactions on Electrical Eng., Electronics, and Communications, 8 (1), 43-52.
    Search in Google Scholar
  22. Singh, B. N., Tiwari, A. K. (2006). Optimal selection of wavelet basis function applied to ECG signal denoising. Digit. Signal Process., 16 (3), 275-287.10.1016/j.dsp.2005.12.003
    Search in Google Scholar
  23. Phinyomark, A., Hirunviriya, S., Limsakul, C., Phukpattaranont, P. (2010). Evaluation of EMG feature extraction for hand movement recognition based on euclidean distance and standard deviation. In 7th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, May 2010. Chiang Mai, Thailand, 856-860.
    Search in Google Scholar
  24. Phinyomark, A., Limsakul, C., Phukpattaranont, P. (2009). A novel feature extraction for robust EMG pattern recognition. J. Comput., 1 (1), 71-80.
    Search in Google Scholar
  25. Zardoshti-Kermani, M., Wheeler, B. C., Badie, K., Hashemi, R. M. (1995). EMG feature evaluation for movement control of upper extremity prostheses. IEEE Trans. Rehabil. Eng., 3 (4), 324-333.10.1109/86.481972
    Search in Google Scholar
  26. Misiti, M., Misiti, Y., Oppenheim, G., Poggi, J.-M. (2010). Wavelet Toolbox User's Guide. The MathWorks, Inc. Retrieved June 28, 2010, from MathWorks http://www.mathworks.com/access/helpdesk/help/pdf_doc/wavelet/wavelet_ug.pdf
    Search in Google Scholar
DOI: https://doi.org/10.2478/v10048-011-0009-y | Journal eISSN: 1335-8871
Journal RSS Feed
Language: English
Page range: 45 - 52
Published on: Jun 3, 2011
Published by: Slovak Academy of Sciences, Institute of Measurement Science
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
Keywords:
Electromyography signal,
EMG,
feature extraction,
wavelet transform,
mean absolute value,
root mean square,
multi-resolution analysis
Related subjects:
Engineering,
Electrical engineering,
Control engineering, metrology and testing

© 2011 A. Phinyomark, C. Limsakul, P. Phukpattaranont, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons License.

Volume 11 (2011): Issue 2 (April 2011)