Skip to main content
SpringerLink
Log in
Book cover

International Conference of Reliable Information and Communication Technology

IRICT 2018: Recent Trends in Data Science and Soft Computing pp 117–127Cite as

Comparative Study of Segmentation and Feature Extraction Method on Finger Movement

  • Conference paper
  • First Online:

  • 2268 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 843))

Abstract

Myoelectric control prostheses fingers are a popular developing clinical option that offers an amputee person to control their artificial fingers by recognizing the contacting muscle residual informs of electromyography (EMG) signal. Lower performance of recognition system always has been the main problem in producing the efficient prostheses finger. This is due to the inefficiency of segmentation and feature extraction in EMG recognition system. This paper aims to compare the most used overlapping segmentation scheme and time domain feature extraction method in recognition system respectively. A literature review found that a combination of Hudgins and Root Mean Square (RMS) methods is a possible way of improving feature extraction. To proof this hypothesis, an experiment was conducted by using a dataset of ten finger movements that has been pre-processed. The performance measurement considered in this study is the classification accuracy. Based on the classification accuracy results for the three common overlapping segmentation schemes, the smaller the window size with larger increment windows produce better accuracy but it will degrade the computational time. For feature extraction, the proposed Hudgins with RMS feature showed an improvement of average accuracy for ten finger movements by 0.74 and 3 per cent compared to Hudgins and RMS alone. Future study should incorporate more advance classification accuracy to improve the study.

This is a preview of subscription content, 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   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

References

  1. Kuiken, T.A., Li, G., Lock, B.A., Lipschutz, R.D., Miller, L.A., Stubblefield, K.A., Englehart, K.B.: Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. JAMA 301(6), 619–628 (2009)

    Article  Google Scholar 

  2. Cutti, A.G., Verni, G., Gruppioni, E., Amoresano, A.: Nuove tecnologie e innovazione nelle protesi di arto superiore. Med. Chir. Ortop. 3, 20–33 (2012)

    Google Scholar 

  3. NLLIC Homepage. http://www.amputee-coalition.org. Accessed 23 May 2018

  4. Englehart, Kevin, Hudgins, Bernard: A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 50(7), 848–854 (2003)

    Article  Google Scholar 

  5. Hudgins, B., Parker, P., Scott, R.N.: A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 40, 82–94 (1993). https://doi.org/10.1109/10.204774

    Article  Google Scholar 

  6. Firoozabadi, S.M.P., Oskoei, M.A., Hu, H.: A human–computer interface based on forehead multi-channel bio-signals to control a virtual wheelchair. In: Proceedings of the 14th Iranian Conference on Biomedical Engineering (ICBME), pp. 272–277 (2008)

    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. Austral. Phys. Eng. Sci. Med. 39(1), 85–102 (2016)

    Article  Google Scholar 

  8. Zawawi, T.N.S.T., Abdullah, A.R., Shair, E.F., Halim, I., Rawaida, O.: Electromyography signal analysis using spectrogram. In: 2013 IEEE Student Conference on Research and Development (SCOReD), pp. 319–324. IEEE (2013)

    Google Scholar 

  9. Reaz, M.B.I., Hussain, M.S., Mohd-Yasin, F.: Techniques of EMG signal analysis: detection, processing, classification and applications. Biol. Proced. Online 8(1), 11 (2006)

    Article  Google Scholar 

  10. Du, S., Vuskovic, M.: Temporal vs. spectral approach to feature extraction from prehensile EMG signals. In: Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, IRI 2004, pp. 344–350. IEEE (2004)

    Google Scholar 

  11. Hargrove, L.J., Englehart, K., Hudgins, B.: A comparison of surface and intramuscular myoelectric signal classification. IEEE Trans. Biomed. Eng. 54(5), 847–853 (2007)

    Article  Google Scholar 

  12. Veer, K., Sharma, T.: A novel feature extraction for robust EMG pattern recognition. J. Med. Eng. Technol. 40(4), 149–154 (2016)

    Article  Google Scholar 

  13. 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 (2002)

    Google Scholar 

  14. Tang, Z., Zhang, K., Sun, S., Gao, Z., Zhang, L., Yang, Z.: An upper-limb power-assist exoskeleton using proportional myoelectric control. Sensors 14(4), 6677–6694 (2014)

    Article  Google Scholar 

  15. Xie, H.-B., Zheng, Y.-P., Guo, J.-Y.: Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition for multifunction prosthesis control. Physiol. Meas. 30(5), 441 (2009)

    Article  Google Scholar 

  16. Chan, A.D.C., Green, G.C.: Myoelectric control development toolbox. In: 30th Conference of the Canadian Medical & Biological Engineering Society, Toronto, Canada, M0100 (2007)

    Google Scholar 

  17. Pancholi, S., Joshi, A.M.: Portable EMG data acquisition module for upper limb prosthesis application. IEEE Sens. J. (2018)

    Google Scholar 

  18. Khushaba, R.N., Takruri, M., Kodagoda, S., Dissanayake, G.: Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Syst. Appl. 39(12), 10731–10738 (2012)

    Article  Google Scholar 

  19. Boostani, R., Moradi, M.H.: Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiol. Meas. 24(2), 309 (2009)

    Article  Google Scholar 

  20. Zardoshti-Kermani, M., Wheeler, B.C., Badie, K., Hashemi, R.M.: EMG feature evaluation for movement control of upper extremity prostheses. IEEE Trans. Rehabil. Eng. 3(4), 324–333 (1995)

    Article  Google Scholar 

  21. Saikia, A., Mazumdar, S., Sahai, N., Paul, S., Bhatia, D.: Comparative study and feature extraction of the muscle activity patterns in healthy subjects. In: 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 147–151. IEEE (2015)

    Google Scholar 

  22. Leon, M., Gutierrez, J.M., Leija, L., Munoz, R:. EMG pattern recognition using support vector machines classifier for myoelectric control purposes. In: 2011 Pan American Health Care Exchanges (PAHCE), pp. 175–178. IEEE (2011)

    Google Scholar 

  23. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)

    Book  Google Scholar 

  24. Phinyomark, A., Scheme, E.: A feature extraction issue for myoelectric control based on wearable EMG sensors. In: 2018 IEEE Sensors Applications Symposium (SAS), pp. 1–6. IEEE (2018)

    Google Scholar 

Download references

Acknowledgements

The author greatly acknowledge the research management center, UTM for financial support through the research university grant scheme (RUG) Vot No Q.J 13000.2528.18H53.

Author information

Authors and Affiliations

  1. Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia

    Nurazrin Mohd Esa, Azlan Mohd Zain, Mahadi Bahari & Suhaila Mohd Yusuf

Authors
  1. Nurazrin Mohd Esa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Azlan Mohd Zain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahadi Bahari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Suhaila Mohd Yusuf
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Nurazrin Mohd Esa , Azlan Mohd Zain , Mahadi Bahari or Suhaila Mohd Yusuf .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. Information Systems Department, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia

    Nadhmi Gazem

  3. Information Technology Department, Faculty of Engineering and Information Technology, Taiz University, Taiz, Yemen

    Fathey Mohammed

  4. Information Systems Department, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia

    Abdelsalam Busalim

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Esa, N.M., Zain, A.M., Bahari, M., Yusuf, S.M. (2019). Comparative Study of Segmentation and Feature Extraction Method on Finger Movement. In: Saeed, F., Gazem, N., Mohammed, F., Busalim, A. (eds) Recent Trends in Data Science and Soft Computing. IRICT 2018. Advances in Intelligent Systems and Computing, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-319-99007-1_12

Download citation

Publish with us

Policies and ethics

Search

Navigation