Skip to main content
SpringerLink
Log in

Electromyography Signal Acquisition, Processing, Optimization and Its Applications

  • Conference paper
  • First Online:
New Approaches for Multidimensional Signal Processing (NAMSP 2022)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 332))

  • 378 Accesses

Abstract

With the ever-increasing role of biomedical signals in the field of Science and technology, electromyogram approach is considered as important technique using EMG signals to monitor muscular activities for stress detection abnormalities and activation level and to study the biomechanics of various movements. EMG signal acquisition and the processing part are being updated day by day in terms of accuracy and artifact removal which makes the analyses part more reliable. This paper discusses the efficient EMG signal acquisition, processing, feature extraction, classification and optimization methods to attain high recognition accuracy using EMG signals.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rajat, E.S., Iqbal, K., White, G., Holtz, J.K.: A review of EMG techniques for detection of gait disorders. In: Artificial Intelligence—Applications in Medicine and Biology, pp. 1–23 (2019)

    Google Scholar 

  2. Peter, K.: The ABC of EMG: A Practical Introduction to Kinesiological Electromyography. NoeraxonInc USA (2005)

    Google Scholar 

  3. Ayten, O.A., Niraj, K.J.: Keep the stress away with SoDA: stress detection and alleviation system. IEEE Trans. Multi-Scale Comput. Syst. 3, 269–282 (2017)

    Article  Google Scholar 

  4. Lara, M.M., Giuseppe, D.V., Madeleine, M.L.: Analysis and biophysics of surface EMG for physiotherapists and kinesiologists: toward a common language with rehabilitation engineers. Front. Neurol. (2020). https://doi.org/10.3389/fneur.2020.576729

    Article  Google Scholar 

  5. Kamen, G., Caldwell, G.E.: Physiology and interpretation of the electromyogram. J. Clin. Neurophysiol. 13, 366–384 (1996)

    Article  Google Scholar 

  6. Toshio, M., Jan, D.S., Roberto, M.: Basic physiology and biophysics of EMG signal generation. In: Electromyography Physiology Engineering and Noninvasive Applications, pp. 1–25. IEEE Press Piscataway, NJ (2004). https://doi.org/10.1002/0471678384.ch1

  7. Dario, F., D., Roberto, M., Dick, F.S.: Biophysics of the generation of EMG signals. In: Electromyography Physiology Engineering and Noninvasive Applications, pp. 81–105. IEEE Press Piscataway, NJ (2004). https://doi.org/10.1002/0471678384.ch4

  8. Javier, R.F.: Understanding the electrical behavior of the action potential in terms of elementary electrical sources. Adv. Physiol. Educ. 39, 15–26 (2015)

    Article  Google Scholar 

  9. Bert, U.K., Johannes, P.V.D., Machiel, J.Z., Dick, F.S.: Inter-operator agreement in decomposition of motor unit firings from high-density surface EMG. J. Electromyogr. Kinesiol. 18, 652–661 (2008)

    Article  Google Scholar 

  10. Aldo, O.P.: Anatomical Guide for the Electromyographer: The Limbs and Trunk, 5th edn. Charles C Thomas Pub Ltd (2011)

    Google Scholar 

  11. Jamileh, Y., Andrew-Hamilton, W.: Characterizing EMG data using machine-learning tools. Comput. Biol. Med. 51, 1–13 (2014)

    Google Scholar 

  12. Yong, N., Xiangjun, Z., Shanan, Z., Yingchun, Z.: Surface EMG decomposition based on K-means clustering and convolution kernel compensation. IEEE J. Biomed. Health Inform. 19(2), 471–477 (2015)

    Article  Google Scholar 

  13. Reaz, M.B.I., Hussain, M.S., Mohd-Yasin, F.: Techniques of EMG signal analysis: detection, processing, classification and applications. Biol. Proced. Online 8, 11–35 (2006)

    Google Scholar 

  14. Reema, J., Vijay, K.G.: Review of electromyography signal with detection, decomposition, features and classifier. Theor. Int. J. Comput. Sci. Eng. 7(5), 487–500 (2019)

    Google Scholar 

  15. Lanyi, X., Andy, A.: An improved method for muscle activation detection during gait. In: Canadian Conference of Electrical and Computer Engineering, vol. 1, pp. 357–360 (2004)

    Google Scholar 

  16. Zvokelj, M., Zupan, S., Prebil, I.: EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis. J. Sound Vib. 370, 394–423 (2016)

    Article  Google Scholar 

  17. Ales, H., Dario, F., Marco, G., Roberto, M., Damjun, Z.: Estimating motor unit discharge patterns from high-density surface electromyogram. Clin. Neurophysiol.: Off. J. Int. Fed. Clin. Neurophysiol. 120(3), 551–562 (2009)

    Article  Google Scholar 

  18. Manoj, D., Chanderpal, S., Dinesh, B.: Study of signal processing techniques for EMG analysis. Int. J. Biomechatronics Biomed. Robot. 1(3), 141–148 (2011)

    Article  Google Scholar 

  19. Jack, K.: Wavelet Analysis and Classification Surface Electromyography Signals. Auckland University of Technology, Auckland, New Zealand (2005)

    Google Scholar 

  20. Luo, Z.Z., Shen, H.X.: Electronic signal denoising method based on Hermite interpolation for wavelet modulus maximum reconstruction. J. Electron. Inf. Technol. 31(4), 857–860 (2009)

    Google Scholar 

  21. Gaofeng, W., Feng, T., Gang, T., Chengtao, W.: A wavelet-based method to predict muscle forces from surface electromyography signals in weight lifting. J. Bionic Eng. 9(1), 48–58 (2012)

    Article  Google Scholar 

  22. Chan, A.D.C., Englehart, K., Hudgins, B., Lovely, D.F.: Myoelectric signals to augment speech recognition. Med. Biol. Eng. Comput. 39, 500–504 (2001)

    Google Scholar 

  23. Hichem, B., Abdenour, H.G., Philippe, R., Karim, A.M., Olivier, B.: Surface EMG signal classification for Parkinson’s disease using WCC descriptor and ANN classifier. In: Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods, pp. 287–294. Science and Technology Publications, Lda (2021)

    Google Scholar 

  24. Abbaspour, S., Lindén, M., Gholamhosseini, H., Naber, A., Ortiz-Catalan, M.: Evaluation of surface EMG-based recognition algorithms for decoding hand movements. Med. Biol. Eng. Comput. 58(1), 83–100 (2019). https://doi.org/10.1007/s11517-019-02073-z

    Article  Google Scholar 

  25. Annachiara, S., Federica, V., Laura, B., Sandro, F., Francesco, D.N.: Time-frequency analysis of surface EMG signals for maximum energy localization during walking. In: EMBEC and NBC 2017, pp. 978–981. Springer, Singapore (2017)

    Google Scholar 

  26. She, Q.S., Ma, P.G., Ma, Y.L., et al.: EMG feature extraction method based on tensor linear Laplacian discriminant. J. Southeast Univ. 47(6), 1117–1121 (2017)

    Google Scholar 

  27. Satish, M., Ruta, K., Faruk, K., Nongmaithem, M.S.: Hand motion recognition from single channel surface EMG using wavelet and artificial neural network. Procedia Comput. Sci. 49, 58–65 (2015)

    Google Scholar 

  28. Ercan, G., Abdulhamit, S.: Comparison of decision tree algorithms for EMG signals classification using DWT. Biomed. Signal Process. Control 18, 138–144 (2015)

    Article  Google Scholar 

  29. Yipeng, Y., Lin, S., Feida, Z., et al.: Multi-feature fusion sEMG pattern recognition based on wavelet transform. J. Transduct. Technol. 29(4), 512–518 (2016)

    Google Scholar 

  30. Al Omari, F., Hui, J., Mei, C., Liu, G.: Pattern recognition of eight hand motions using feature extraction of forearm EMG signal. Proc. Natl. Acad. Sci., India, Sect. A 84(3), 473–480 (2014). https://doi.org/10.1007/s40010-014-0148-2

    Article  Google Scholar 

  31. Zhang, H.K., Dawei, W., Yang, H., et al.: Surface EMG signal acquisition and motion recognition system. Mech. Des. Manuf. 3(8), 38–40 (2013)

    Google Scholar 

  32. Muthusamy, H., Chong, Y.F., Sindhu, R., Bukhari, I., Sazali, Y.: A comparative study of wavelet families for classification of wrist motions. Comput. Electr. Eng. 38(6), 1798–1807 (2012)

    Article  Google Scholar 

  33. Ganesh, N., Suviseshamuthu, E.S., Hung, T.N.: Single-channel EMG classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorders. IEEE Trans. Neural Syst. Rehabil. Eng. 24(7), 734–743 (2016)

    Google Scholar 

  34. Venkatraman, R.: Overview of artificial neural network models in the biomedical domain. Bratisl. Med. J. 120(7), 536–540 (2019)

    Google Scholar 

  35. Ram, M.S., Vivek, A., Sangeeta, C., Amod, K.: Comparative analysis of SVM and ANN classifier based on surface EMG signals for elbow movement classification. J. Interdiscip. Math. 153–161 (2020)

    Google Scholar 

  36. Xiuwu, S., Kelvin, W., Yun, Z.: Pattern recognition of SEMG based on wavelet packet transform and improved SVM. Optik 176, 228–235 (2019)

    Article  Google Scholar 

  37. Reema, J., Vijay, K.G.: Review of EMG signal classification approaches based on various feature domain. Matter: Int. J. Sci. Technol. 6(3), 123–143 (2021)

    Google Scholar 

  38. Virendra, P.M., Prashant, K., Suman, H.: Optimisation and classification of EMG signal using PSO-ANN. In: Devices for Integrated Circuit (DevIC), Kalyani, India, pp. 191–195 (2019)

    Google Scholar 

  39. Andrew, V., Israel, H., Gregory, L., Gabriel, S.T., Taian, M.V.: Interpreting signal amplitudes in surface electromyography studies in sport and rehabilitation sciences. Front. Physiol. 8(985), 1–15 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, Haryana, India

    Amol Kumar & Manoj Duhan

  2. Department of Biomedical Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, Haryana, India

    Poonam Sheoran

Authors
  1. Amol Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manoj Duhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Poonam Sheoran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amol Kumar .

Editor information

Editors and Affiliations

  1. Technical University of Sofia, Sofia, Bulgaria

    Roumen Kountchev

  2. Technical University of Sofia, Sofia, Bulgaria

    Rumen Mironov

  3. University of Hyogo, Kobe, Japan

    Kazumi Nakamatsu

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kumar, A., Duhan, M., Sheoran, P. (2023). Electromyography Signal Acquisition, Processing, Optimization and Its Applications. In: Kountchev, R., Mironov, R., Nakamatsu, K. (eds) New Approaches for Multidimensional Signal Processing. NAMSP 2022. Smart Innovation, Systems and Technologies, vol 332. Springer, Singapore. https://doi.org/10.1007/978-981-19-7842-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-7842-5_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-7841-8

  • Online ISBN: 978-981-19-7842-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation