Skip to main content

Advertisement

Springer Nature Link
Log in

Local binary patterns for noise-tolerant sEMG classification

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

One-dimensional local binary pattern (1DLBP) has been recently specialized for feature extraction from different types of 1D biological signals. One of the major drawbacks of using 1DLBP, which unavoidably results in classification accuracy reduction, is its noise sensitivity due to the thresholding mechanism. To overcome this deficiency, we have proposed a new one-dimensional noise-tolerant binary pattern (1DNTBP) in this paper. In contrast to 1DLBP, our proposed operator has been defined to use information of a sampling interval as a threshold instead of using central sample value. In order to evaluate 1DNTBP, we applied our proposed feature extraction method on sEMG for basic hand movement dataset. Additionally, a feature selection stage has been considered to perform further noise removal and insignificant patterns reduction process. Hereafter, a variety of classifiers have been tested with the aim of categorizing the selected features. Experimental results indicate that not only does the proposed operator provide noise tolerance, but also it works adaptably well with various classifiers causing it to be a universal operator, sufficiently appropriate to be applied to different applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Al-Assaf, Y.: Surface myoelectric signal analysis: dynamic approaches for change detection and classification. IEEE Trans. Biomed. Eng. 53(11), 2248–2256 (2006)

    Article  Google Scholar 

  2. Alahmadi, A., Hussain, M., Aboalsamh, H., Muhammad, G., Bebis, G., Mathkour, H.: Passive detection of image forgery using DCT and local binary pattern. Signal Image Video Process. 11(1), 81–88 (2017)

    Article  Google Scholar 

  3. Bingham, E., Kaski, S., Laaksonen, J., Lampinen, J.: Advances in Independent Component Analysis and Learning Machines. Academic Press, Cambridge (2015)

    Google Scholar 

  4. Chatlani, N., Soraghan, J.J.: Local binary patterns for 1-d signal processing. In: Signal Processing Conference, 2010 18th European, pp. 95–99. IEEE (2010)

  5. Ertuğrul, Ö.F., Kaya, Y., Tekin, R.: A novel approach for SEMG signal classification with adaptive local binary patterns. Med. Biol. Eng. Comput. 54(7), 1137–1146 (2016)

    Article  Google Scholar 

  6. Ertuğrul, Ö.F., Kaya, Y., Tekin, R., Almalı, M.N.: Detection of Parkinson’s disease by shifted one dimensional local binary patterns from gait. Expert Syst. Appl. 56, 156–163 (2016)

    Article  Google Scholar 

  7. Fariman, H.J., Ahmad, S.A., Marhaban, M.H., Ghasab, M.A., Chappell, P.H.: Hand movements classification for myoelectric control system using adaptive resonance theory. Australas. Phys. Eng. Sci. Med. 39(1), 85–102 (2016)

    Article  Google Scholar 

  8. Hafiane, A., Seetharaman, G., Zavidovique, B.: Median binary pattern for textures classification. In: International Conference Image Analysis and Recognition, pp. 387–398. Springer (2007)

  9. Heikkila, M., Pietikainen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)

    Article  Google Scholar 

  10. Houam, L., Hafiane, A., Boukrouche, A., Lespessailles, E., Jennane, R.: One dimensional local binary pattern for bone texture characterization. Pattern Anal. Appl. 17(1), 179–193 (2014)

    Article  MathSciNet  Google Scholar 

  11. Jin, H., Liu, Q., Lu, H., Tong, X.: Face detection using improved LBP under bayesian framework. In: Third International Conference on Image and Graphics (ICIG’04), pp. 306–309. IEEE (2004)

  12. Kaya, Y., Ertuğrul, Ö.F.: Gender classification from facial images using gray relational analysis with novel local binary pattern descriptors. Signal Image Video Process. 11(4), 769–776 (2017)

    Article  Google Scholar 

  13. Kaya, Y., Uyar, M., Tekin, R., Yıldırım, S.: 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Appl. Math. Comput. 243, 209–219 (2014)

    MathSciNet  MATH  Google Scholar 

  14. Kendell, C., Lemaire, E.D., Losier, Y., Wilson, A., Chan, A., Hudgins, B.: A novel approach to surface electromyography: an exploratory study of electrode-pair selection based on signal characteristics. J. Neuroeng. Rehabil. 9(1), 24 (2012)

    Article  Google Scholar 

  15. Khushaba, R.N., Al-Timemy, A., Kodagoda, S., Nazarpour, K.: Combined influence of forearm orientation and muscular contraction on EMG pattern recognition. Expert Syst. Appl. 61, 154–161 (2016)

    Article  Google Scholar 

  16. Kumar, T.S., Kanhangad, V., Pachori, R.B.: Classification of seizure and seizure-free EEG signals using local binary patterns. Biomed. Signal Process. Control 15, 33–40 (2015)

  17. Liu, L., Fieguth, P., Guo, Y., Wang, X., Pietikäinen, M.: Local binary features for texture classification: taxonomy and experimental study. Pattern Recognit. 62, 135–160 (2017)

  18. Lumini, L.N.S.B.A.: Selecting the best performing rotation invariant patterns in local binary/ternary patterns. In: International Conference on IP, Computer Vision, and Pattern Recognition (2010)

  19. Makaremi, M., Razmjooy, N., Ramezani, M.: A new method for detecting texture defects based on modified local binary pattern. Signal Image Video Process. 12(7), 1395–1401 (2018)

    Article  Google Scholar 

  20. Mokhlesabadifarahani, B., Gunjan, V.K.: EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction. Springer, Berlin (2015)

    Book  Google Scholar 

  21. Nanni, L., Lumini, A., Brahnam, S.: Local binary patterns variants as texture descriptors for medical image analysis. Artif. Intell. Med. 49(2), 117–125 (2010)

    Article  MATH  Google Scholar 

  22. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

  23. Phinyomark, A., Limsakul, C., Phukpattaranont, P.: Application of wavelet analysis in emg feature extraction for pattern classification. Meas. Sci. Rev. 11(2), 45–52 (2011)

    Article  Google Scholar 

  24. Phinyomark, A., Phukpattaranont, P., Limsakul, C.: Feature reduction and selection for EMG signal classification. Expert Syst. Appl. 39(8), 7420–7431 (2012)

    Article  Google Scholar 

  25. Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns, vol. 40. Springer, Berlin (2011)

    Google Scholar 

  26. Ren, J., Jiang, X., Yuan, J.: Noise-resistant local binary pattern with an embedded error-correction mechanism. IEEE Trans. Image Process. 22(10), 4049–4060 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  27. Sapsanis, C., Georgoulas, G., Tzes, A., Lymberopoulos, D.: Improving EMG based classification of basic hand movements using EMD. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5754–5757. IEEE (2013)

  28. Sapsanis, C., Tzes, A.: semg for basic hand movement dataset (2017). https://archive.ics.uci.edu/ml/datasets/sEMG+for+Basic+Hand+movements. Accessed 2 Jan 2017

  29. Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  30. Tkach, D., Huang, H., Kuiken, T.A.: Study of stability of time-domain features for electromyographic pattern recognition. J. Neuroeng. Rehabil. 7(1), 21 (2010)

    Article  Google Scholar 

  31. Zecca, M., Micera, S., Carrozza, M.C., Dario, P.: Control of multifunctional prosthetic hands by processing the electromyographic signal. Crit. Rev. Biomed. Eng. 30(4–6), 459–485 (2002)

    Article  Google Scholar 

  32. Zhao, Y., Jia, W., Hu, R.X., Min, H.: Completed robust local binary pattern for texture classification. Neurocomputing 106, 68–76 (2013)

    Article  Google Scholar 

  33. Zhu, Q., Chatlani, N., Soraghan, J.J.: 1-D local binary patterns based VAD used inhmm-based improved speech recognition. In: 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pp. 1633–1637. IEEE (2012)

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, RAZI University, Kermanshah, Iran

    Sayed Mohamad Tabatabaei & Abdolah Chalechale

Authors
  1. Sayed Mohamad Tabatabaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdolah Chalechale
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdolah Chalechale.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tabatabaei, S.M., Chalechale, A. Local binary patterns for noise-tolerant sEMG classification. SIViP 13, 491–498 (2019). https://doi.org/10.1007/s11760-018-1374-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-018-1374-x

Keywords

Search

Navigation