Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Super Wavelet for sEMG Signal Extraction During Dynamic Fatiguing Contractions

  • 322 Accesses

  • 5 Citations

Abstract

In this research an algorithm was developed to classify muscle fatigue content from dynamic contractions, by using a genetic algorithm (GA) and a pseudo-wavelet function. Fatiguing dynamic contractions of the biceps brachii were recorded using Surface Electromyography (sEMG) from thirteen subjects. Labelling the signal into two classes (Fatigue and Non-Fatigue) aided in the training and testing phase. The genetic algorithm was used to develop a pseudo-wavelet function that can optimally decompose the sEMG signal and classify the fatigue content of the signal. The evolved pseudo wavelet was tuned using the decomposition of 70 % of the sEMG trials. 28 independent pseudo-wavelet evolution were run, after which the best run was selected and then tested on the remaining 30 % of the trials to measure the classification performance. Results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.45 percentage points to 14.95 percentage points when compared to other standard wavelet functions (p<0.05), giving an average correct classification of 87.90 %.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. 1.

    Stulen, F.B., and De Luca, C.J., Muscle fatigue monitor: a noninvasive device for observing localized muscular fatigue. IEEE Trans. Biomed. Eng. 29:760–768, 1982.

  2. 2.

    Merletti, R., and Parker, P.A., Electromyography: physiology, engineering and non-invasive applications. New York: John Wiley, 2004.

  3. 3.

    Konrad, P., The ABC of EMG: a practical introduction to kinesiological electromyography. USA: Noraxon, Inc., 2005.

  4. 4.

    Jorgensen, K., Fallentin, N., Krogh-Lund, C., Jensen, B., Electromyography and fatigue during prolonged, low-level static contractions. Eur. J. Appl. Physiol. Occup. Physiol. 57:316–321, 1988.

  5. 5.

    Rainoldi, A., Nazzaro, M., Merletti, R., Farina, D., Caruso, I., Gaudenti, S., Geometrical factors in surface EMG of the vastus medialis and lateralis muscles. J. Electromyogr. Kinesiol. 10:327–336, 2000.

  6. 6.

    Hagberg, M., Work load and fatigue in repetitive arm elevations. Ergonomics 24:543–555, 1981.

  7. 7.

    Asghari Oskœi, M., Hu, H., Gan, J.Q.: Manifestation of fatigue in myoelectric signals of dynamic contractions produced during playing PC games, in: Proceedings of the 30th annual international IEEE EMBS conference, IEEE Engineering in Medicine and Biology Society, 2008, pp. 315–318

  8. 8.

    Sparto, P.J., Parnianpour, M., Barria, E.A., Jagadeesh, J.M., Wavelet analysis of electromyography for back muscle fatigue detection during isokinetic constant-torque exertions. Spine 24:1791–1798, 1999.

  9. 9.

    Bonato, P., Roy, S.H., Knaflitz, M., De Luca, C.J., Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions. IEEE Trans. Biomed. Eng. 48:745–753, 2001.

  10. 10.

    Karlsson, S., Yu, J., Akay, M., Enhancement of spectral analysis of myoelectric signals during static contractions using wavelet methods. IEEE Trans. Biomed. Eng. 46:670–684, 1999.

  11. 11.

    Singh, V.P., Kumar, D.K., Polus, B., Fraser, S., Strategies to identify changes in SEMG due to muscle fatigue during cycling. J. Med. Eng. Technol. 31:144–151, 2007.

  12. 12.

    Farina, D., Interpretation of the surface electromyogram in dynamic contractions. Exerc. Sport Sci. Rev. 34: 121–127, 2006.

  13. 13.

    Dimitrov, G.V., Arabadzhiev, T.I., Mileva, K.N., Bowtell, J.L., Crichton, N., Dimitrova, N.A., Muscle fatigue during dynamic contractions assessed by new spectral indices. Med. Sci. Sports Exerc. 38:1971–1979, 2006.

  14. 14.

    Guglielminotti, P., and Merletti, R.: Effect of electrode location on surface myoelectric signal variables: a simulation study, in: 9th international congress of The International Society of Electrophysiological Kinesiology, Florence, Italy (1992)

  15. 15.

    Kumar, D.K., Pah, N.D., Bradley, A., Wavelet analysis of surface electromyography to determine muscle fatigue. IEEE Trans. Neural Syst. Rehabil. Eng. 11:400–406, 2003.

  16. 16.

    Khezri, M., and Jahed, M.: Real-time intelligent pattern recognition algorithm for surface EMG signals, Biomedical Engineering Online

  17. 17.

    Gler, N., and Koer, S., Classification of emg signals using pca and fft. J. Med. Syst. 29(3):241–250, 2005. doi:10.1007/s10916-005-5184-7.

  18. 18.

    Chen, M., Guan, J., Liu, H., Enabling fast brain-computer interaction by single-trial extraction of visual evoked potentials. Journal of Medical Systems 35(5):1323–1331, 2011. doi:10.1007/s10916-011-9696-z.

  19. 19.

    Raez, M.B., Hussain, M.S., Mohd-Yasin, F., Techniques of EMG signal analysis: detection, processing, classification and applications. Biological Proceedings Online 8:11–35, 2006.

  20. 20.

    Wang, G., Yan, Z., Hu, X., Xie, H., Wang, Z., Classification of surface EMG signals using harmonic wavelet packet transform. Physiol. Meas. 27:1255–1267, 2006.

  21. 21.

    Kattan, A., Al-Mulla, M., Sepulveda, F., Poli, R.: Detecting localised muscle fatigue during isometric contraction using genetic programming., in: IJCCI, 2009, pp. 292–297

  22. 22.

    Al-Mulla, M.R., Sepulveda, F., Colley, M., Kattan, A.: Classification of localized muscle fatigue with genetic programming on sEMG during isometric contraction, Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC (2009)

  23. 23.

    Al-Mulla, M.R.: Evolutionary computation extracts a super semg feature to classify localized muscle fatigue during dynamic contractions, in: Computer Science and Electronic Engineering Conference (CEEC), 2012 4th, 2012, pp. 220–224. doi:10.1109/CEEC.2012.6375409

  24. 24.

    Al-Mulla, M.R., Sepulveda, F., Colley, M., Evolved pseudo-wavelet function to optimally decompose sEMG for automated classification of localized muscle fatigue. Med. Eng. Phys. 33(4):411–417, 2011.

  25. 25.

    Al-Mulla, M.R., and Sepulveda, F., Novel pseudo-wavelet function for mmg signal extraction during dynamic fatiguing contractions. Sensors 14(6):9489–9504, 2014.

  26. 26.

    Al-Mulla, M.R., Sepulveda, F., Colley, M., Al-Mulla, F.: Statistical class separation using sEMG features towards automated muscle fatigue detection and prediction, in: International Congress on Image and Signal Processing, 2009, pp. 1–5. doi:10.1109/CISP.2009.5304091

  27. 27.

    Al-Mulla, M.R., and Sepulveda, F.: A Novel Feature Assisting in the Prediction of sEMG Muscle Fatigue Towards a Wearable Autonomous System, Proceedings of the 16th IEEE International Mixed-Signals, Sensors and Systems Test Workshop (IMS3TW’10), France

  28. 28.

    Al-Mulla, M.R., and Sepulveda, F., Novel feature modelling the prediction and detection of semg muscle fatigue towards an automated wearable system. Sensors 10(5):4838–4854, 2010. doi:10.3390/s100504838.

  29. 29.

    Al-Mulla, M.R., and Sepulveda, F.: Predicting the time to localized muscle fatigue using ANN and evolved sEMG feature, IEEE International Conference on Autonomous and Intelligent Systems, (AIS 2010), Povoa de Varzim, Portugal (2010) 1–6

  30. 30.

    Subasi, A., and Kiymik, M., Muscle fatigue detection in emg using timefrequency methods, ica and neural networks. J. Med. Syst. 34(4):777–785, 2010. doi:10.1007/s10916-009-9292-7.

  31. 31.

    Al-Mulla, M.R., Sepulveda, F., Colley, M., An autonomous wearable system for predicting and detecting localised muscle fatigue. Sensors (Basel) 11(2):1542–1557, 2011.

  32. 32.

    Walker, J.S., A primer on wavelets and their scientific applications. Boca Raton Fla: Chapman and Hall/CRC, 2000.

  33. 33.

    Michalewicz, Z., Genetic algorithms + data structures = evolution programs. New York: Springer-Verlag, 1996.

  34. 34.

    Sepulveda, F., Meckes, M., Conway, B., Cluster separation index suggests usefulness of non-motor eeg channels in detecting wrist movement direction intention, in: EEE Conference on Cybernetics and Intelligent Systems, pp. 943–947: IEEE Press , 2004.

  35. 35.

    Masuda, K., Masuda, T., Sadoyama, T., Inaki, M., Katsuta, S., Changes in surface EMG parameters during static and dynamic fatiguing contractions. J. Electromyogr. Kinesiol. 9:39–46 , 1999.

  36. 36.

    Farina, D., Merletti, R., Enoka, R.M., The extraction of neural strategies from the surface EMG. J. Appl. Physiol. 96:1486–1495, 2004.

Download references

Author information

Correspondence to Mohamed R. Al-Mulla.

Additional information

This article is part of the Topical Collection on Mobile Systems

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Al-Mulla, M.R., Sepulveda, F. Super Wavelet for sEMG Signal Extraction During Dynamic Fatiguing Contractions. J Med Syst 39, 167 (2015). https://doi.org/10.1007/s10916-014-0167-1

Download citation

Keywords

  • Genetic algorithms
  • Localised muscle fatiguen
  • EMG
  • Wavelet analysis
  • Pseudo wavelets