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Movement Identification in EMG Signals Using Machine Learning: A Comparative Study

  • Laura Lasso-Arciniegas
  • Andres Viveros-Melo
  • José A. Salazar-Castro
  • Miguel A. Becerra
  • Andrés Eduardo Castro-Ospina
  • E. Javier Revelo-Fuelagán
  • Diego H. Peluffo-Ordóñez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11047)

Abstract

The analysis of electromyographic (EMG) signals enables the development of important technologies for industry and medical environments, due mainly to the design of EMG-based human-computer interfaces. There exists a wide range of applications encompassing: Wireless-computer controlling, rehabilitation, wheelchair guiding, and among others. The semantic interpretation of EMG analysis is typically conducted by machine learning algorithms, and mainly involves stages for signal characterization and classification. This work presents a methodology for comparing a set of state-of-the-art approaches of EMG signal characterization and classification within a movement identification framework. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification performance of (90.89 ± 1.12)% (KNN), (93.92 ± 0.34)% (ANN) and 91.09 ± 0.93 (Parzen-density-based classifier) with 12 movements.

Keywords

ANN EMG signals Feature extraction KNN Parzen 

Notes

Acknowledgments

This work is supported by the “Smart Data Analysis Systems - SDAS” group (http://sdas-group.com), as well as the “Grupo de Investigación en Ingeniería Eléctrica y Electrónica - GIIEE” from Universidad de Nariño. Also, the authors acknowledge to the research project supported by Agreement No. 095 November 20th, 2014 by VIPRI from Universidad de Nariño.

References

  1. 1.
    Phinyomark, A., Phukpattaranont, P., Limsakul, C.: A review of control methods for electric power wheelchairs based on electromyography signals with special emphasis on pattern recognition. IETE Tech. Rev. 28(4), 316–326 (2011)CrossRefGoogle Scholar
  2. 2.
    Aguiar, L.F., Bó, A.P.: Hand gestures recognition using electromyography for bilateral upper limb rehabilitation. In: 2017 IEEE Life Sciences Conference (LSC), pp. 63–66. IEEE (2017)Google Scholar
  3. 3.
    Rodrguez-Sotelo, J., Peluffo-Ordoez, D., Cuesta-Frau, D., Castellanos-Domnguez, G.: Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering. Comput. Methods Programs Biomed. 108(1), 250–261 (2012)CrossRefGoogle Scholar
  4. 4.
    Atzori, M., et al.: Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Sci. Data 1, 140053 (2014)CrossRefGoogle Scholar
  5. 5.
    Podrug, E., Subasi, A.: Surface EMG pattern recognition by using DWT feature extraction and SVM classifier. In: The 1st Conference of Medical and Biological Engineering in Bosnia and Herzegovina (CMBEBIH 2015), March 2015, pp. 13–15 (2015)Google Scholar
  6. 6.
    Vicario Vazquez, S.A., Oubram, O., Ali, B.: Intelligent recognition system of myoelectric signals of human hand movement. In: Brito-Loeza, C., Espinosa-Romero, A. (eds.) ISICS 2018. CCIS, vol. 820, pp. 97–112. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-76261-6_8CrossRefGoogle Scholar
  7. 7.
    Atzori, M., et al.: Characterization of a benchmark database for myoelectric movement classification. IEEE Trans. Neural Syst. Rehabil. Eng. 23(1), 73–83 (2015)CrossRefGoogle Scholar
  8. 8.
    Krishna, V.A., Thomas, P.: Classification of EMG signals using spectral features extracted from dominant motor unit action potential. Int. J. Eng. Adv. Technol. 4(5), 196–200 (2015)Google Scholar
  9. 9.
    Negi, S., Kumar, Y., Mishra, V.: Feature extraction and classification for EMG signals using linear discriminant analysis. In: International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Fall), pp. 1–6. IEEE (2016)Google Scholar
  10. 10.
    Phinyomark, A., Limsakul, C., Phukpattaranont, P.: A novel feature extraction for robust EMG pattern recognition. CoRR abs/0912.3973 (2009)Google Scholar
  11. 11.
    Ahlstrom, C., et al.: Feature extraction for systolic heart murmur classification. Ann. Biomed. Eng. 34(11), 1666–1677 (2006)CrossRefGoogle Scholar
  12. 12.
    Han, J.S., Song, W.K., Kim, J.S., Bang, W.C., Lee, H., Bien, Z.: New EMG pattern recognition based on soft computing techniques and its application to control of a rehabilitation robotic arm. In: Proceedings of 6th International Conference on Soft Computing (IIZUKA2000), pp. 890–897 (2000)Google Scholar
  13. 13.
    Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994).  https://doi.org/10.1007/3-540-57868-4_57CrossRefGoogle Scholar
  14. 14.
    Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Machine Learning Proceedings 1992, pp. 249–256. Elsevier (1992)Google Scholar
  15. 15.
    Halaki, M., Ginn, K.: Normalization of EMG signals: to normalize or not to normalize and what to normalize to? (2012)Google Scholar
  16. 16.
    Romo, H., Realpe, J., Jojoa, P., Cauca, U.: Surface EMG signals analysis and its applications in hand prosthesis control. Revista Avances en Sistemas e Informática 4(1), 127–136 (2007)Google Scholar
  17. 17.
    Arozi, M., et al.: Electromyography (EMG) signal recognition using combined discrete wavelet transform based on artificial neural network (ANN). In: International Conference of Industrial, Mechanical, Electrical, and Chemical Engineering (ICIMECE), pp. 95–99. IEEE (2016)Google Scholar
  18. 18.
    Shin, S., Tafreshi, R., Langari, R.: A performance comparison of hand motion EMG classification. In: 2014 Middle East Conference on Biomedical Engineering (MECBME), pp. 353–356. IEEE (2014)Google Scholar
  19. 19.
    Kim, K.S., Choi, H.H., Moon, C.S., Mun, C.W.: Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions. Curr. Appl. Phys. 11(3), 740–745 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Laura Lasso-Arciniegas
    • 1
  • Andres Viveros-Melo
    • 1
  • José A. Salazar-Castro
    • 1
    • 2
  • Miguel A. Becerra
    • 3
  • Andrés Eduardo Castro-Ospina
    • 3
  • E. Javier Revelo-Fuelagán
    • 1
  • Diego H. Peluffo-Ordóñez
    • 2
    • 4
  1. 1.Universidad de NariñoPastoColombia
  2. 2.Corporación Universitaria Autónoma de NariñoPastoColombia
  3. 3.Instituto Tecnológico Metropolitano (ITM)MedellínColombia
  4. 4.Yachay TechUrcuquiEcuador

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