Advertisement

Enhancing the classification of hand movements through sEMG signal and non-iterative methods

  • Vincius Horn CeneEmail author
  • Alexandre Balbinot
Original Paper
  • 22 Downloads

Abstract

In movement classification through surface electromyography signal processing, the classification method must identify the user’s intention with satisfactory accuracy to promote an adequate biosignal interface. Traditionally, classical methods such as Support Vector Machines, Artificial Neural Networks, and Logistic Regression have been used to this end. Recently, Non-Iterative Methods based on Artificial Neural Networks have been revisited in the form of Random Vector Functional-Link Networks (RVFL) and its most recent derivation, the so-called Extreme Learning Machines (ELM). In this work, we evaluate the performance and potentialities of RVFL and ELM with Moore-Penrose (RVFL and ELM) and Ridge-Regression (R-ELM and R-RVFL) methods to classify 17 different upper-limb movements through surface electromyography (sEMG) signal processing. 341 different sets of tests involving sEMG channels and features were performed for each one of the 20 subjects (ten amputees and ten non-amputees) from NINAPro database. Overall, the NIM methods presented consistent advantages of accuracy rate and time processing when compared with most traditional classifiers. Once the best setup of inputs was defined, the R-ELM presented the best accuracy rate. While results up to 80% were already reported for NINAPro data using Deep Learning techniques which are blatantly costly on a computational perspective, there is no evaluation performed in embedded platforms using this database. Therefore, we conducted an embedded study case of the ELM method applied to a Raspberry Pi platform using: a) a timestamp segmentation and b) a sliding-window approach to emulate an online application of the technique. The first trial reached an average accuracy rate of 90.9% for the non-amputee and 63.1% for the amputee subjects. The second trial reached 77.2% of average accuracy for the non-amputee and 55.3% for the amputee subjects, pairing the results in literature, even with the limitations of an embedded platform.

Keywords

sEMG Upper-limb Extreme learning machines Logistic regression Support vector machines Random vector functional-link 

Notes

Acknowledgment

The authors would like to acknowledge the Brazilian Coordination for Improvement of Higher Level Personnel (CAPES) for the provision of the scholarship that made this work possible.

Funding Information

There is no funding source

Compliance with Ethical Standards

Conflict of interests statement

The authors disclose they do not have any relation with other people or organizations that could inappropriately influence (bias) their work, such as employers, stock ownership, paid expert testimony, patent applications/registrations, and grants or other funding.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Vodovnik L, Long C, Reswick JB, Lippay A, Starbuck D. IEEE Trans Biomed Eng 1965; BME-12(3 and 4):169.  https://doi.org/10.1109/TBME.1965.4502374. http://ieeexplore.ieee.org/document/4502374/.CrossRefGoogle Scholar
  2. 2.
  3. 3.
    Triolo RJ, Moskowitz GD. IEEE Trans Biomed Eng 1985;BME-32(3):239.  https://doi.org/10.1109/TBME.1985.325534. http://www.ncbi.nlm.nih.gov/pubmed/3997180.CrossRefGoogle Scholar
  4. 4.
    Zardoshti-Kermani M, Wheeler BC, Badie K, Hashemi RM. IEEE Trans Rehabil Eng 1995;3(4):324.  https://doi.org/10.1109/86.481972. http://ieeexplore.ieee.org/document/481972/.CrossRefGoogle Scholar
  5. 5.
    Carrozza MC, Cappiello G, Stellin G, Zaccone F, Vecchi F, Micera S, Dario P, Superiore S, Anna S. 2005. IEEE/RSJ international conference on intelligent robots and systems.Google Scholar
  6. 6.
    Young AJ, Smith LH, Rouse EJ, Hargrove LJ. IEEE Trans Biomed Eng 2013;60(5):1250.CrossRefGoogle Scholar
  7. 7.
    Smith LH, Kuiken TA, Hargrove LJ. IEEE Trans Biomed Eng 2016;63(4):737.CrossRefGoogle Scholar
  8. 8.
    Atzori M, Gijsberts A, Castellini C, Caputo B, Hager AGM, Elsig S, Giatsidis G, Bassetto F, Müller H. 2014. Scientific Data 1.  https://doi.org/10.1038/sdata.2014.53. http://www.nature.com/articles/sdata201453.
  9. 9.
    Jiang N, Vujaklija I, Rehbaum B, Graimann H, Farina D. IEEE Trans Neural Syst Rehabil Eng 2014;22:252.Google Scholar
  10. 10.
    Favieiro G, Cene VH, Balbinot A. Braziilian Journal of Instrumentation and Control 2016;4(4):21.CrossRefGoogle Scholar
  11. 11.
  12. 12.
  13. 13.
    Cene VH, Balbinot A. 2015. 2015 latin america congress on computational intelligence (LA-CCI).  https://doi.org/10.1109/LA-CCI.2015.7435940.
  14. 14.
    Cene VH, Balbinot A. Brazilian Journal of Instrumentation and Control 2016;4(3):14. https://periodicos.utfpr.edu.br/bjic/article/view/4878/3218.CrossRefGoogle Scholar
  15. 15.
    Geethanjali P. Australas Phys Eng Sci Med 2015;38(2):331.  https://doi.org/10.1007/s13246-015-0343-8.CrossRefGoogle Scholar
  16. 16.
    Riillo F, Quitadamo LR, Cavrini F, Gruppioni E, Pinto CA, Pasto NC, Sbernini L, Albero L, Saggio G. Biomed Signal Process Control 2014;14(1):117.  https://doi.org/10.1016/j.bspc.2014.07.007.CrossRefGoogle Scholar
  17. 17.
    Zhai X, Jelfs B, Chan RH, Tin C. Front Neurosci 2017;11(JUL):1.  https://doi.org/10.3389/fnins.2017.00379.Google Scholar
  18. 18.
    Zhang L, Suganthan PN. Inf Sci 2016;1094:367–368.  https://doi.org/10.1016/j.ins.2015.09.025.Google Scholar
  19. 19.
    Ren Y, Suganthan PN, Srikanth N, Amaratunga G. Inf Sci 2015;1078:367–368.  https://doi.org/10.1016/j.ins.2015.11.039.Google Scholar
  20. 20.
    Pao YH, Park GH, Sobajic DJ. Neurocomputing 1994;6(2):163.  https://doi.org/10.1016/0925-2312(94)90053-1.CrossRefGoogle Scholar
  21. 21.
    Zhang L, Suganthan P. Inf Sci 2016; 364: 146.  https://doi.org/10.1016/j.ins.2016.01.039.CrossRefGoogle Scholar
  22. 22.
    Pao YH, Phillips SM. 1995. Neurocomputing, 6(9).  https://doi.org/10.1016/0925-2312(95)00066-F.
  23. 23.
    Chi HM, Ersoy OK. IEEE Trans Geosci Remote Sens 2005;43(8):1890.  https://doi.org/10.1109/TGRS.2005.851188.CrossRefGoogle Scholar
  24. 24.
    Husmeier D, Taylor JG. Neural Netw 1998;11(1):89.  https://doi.org/10.1016/S0893-6080(97)00089-0.CrossRefGoogle Scholar
  25. 25.
    Park GH, Pao YH. Neurocomputing 2000;31(1-4):45.  https://doi.org/10.1016/S0925-2312(99)00149-6.CrossRefGoogle Scholar
  26. 26.
    Park GH, Lee YJ, Leclair SR. Eng Appl Artif Intell 2000;13(5):565.  https://doi.org/10.1016/S0952-1976(00)00036-1.CrossRefGoogle Scholar
  27. 27.
    Comminiello D, Scarpiniti M, Scardapane S, Parisi R, Uncini A. Neural Netw 2015;69:51.  https://doi.org/10.1016/j.neunet.2015.05.002.CrossRefGoogle Scholar
  28. 28.
    Wang Z, Yoon S, Xie SJ, Lu Y, Park DS. Proceedings of the 2013 6th international congress on image and signal processing. CISP 2013 2013;2(Cisp):773.  https://doi.org/10.1109/CISP.2013.6745269.Google Scholar
  29. 29.
    Wang Z, Yoon S, Xie SJ, Lu Y, Park DS. 2014. Sci World J 2014.  https://doi.org/10.1155/2014/105089.
  30. 30.
    Scardapane S, Panella M, Comminiello D, Uncini A. Procedia Computer Science 2015;53(1):468.  https://doi.org/10.1016/j.procs.2015.07.324.CrossRefGoogle Scholar
  31. 31.
    Scardapane S, Comminiello D, Scarpiniti M, Uncini A. Inf Sci 2016;156:364–365.  https://doi.org/10.1016/j.ins.2015.07.060.Google Scholar
  32. 32.
    Scardapane S, Comminiello D, Scarpiniti M, Uncini A. Inf Sci 2016;156:364–365.  https://doi.org/10.1016/j.ins.2015.07.060.Google Scholar
  33. 33.
  34. 34.
    Huang GB, Zhu Qy, Siew Ck, Ã GbH, Zhu Qy, Siew Ck, Huang GB, Zhu Qy, Siew Ck. Neurocomputing 2006;70(1-3):489.  https://doi.org/10.1016/j.neucom.2005.12.126. http://linkinghub.elsevier.com/retrieve/pii/S0925231206000385.CrossRefGoogle Scholar
  35. 35.
    Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A. IEEE Trans Neural Netw 2010;21(1):158.  https://doi.org/10.1109/TNN.2009.2036259.CrossRefGoogle Scholar
  36. 36.
    Liu H, Gegov A, Stahl F. 2015. Information Granularity, Big Data, and … pp 1–23. http://link.springer.com/chapter/10.1007/978-3-319-08254-7_10.
  37. 37.
    Akusok A, Miche Y, Lendasse A. High-performance extreme learning machines: a complete toolbox for big data applications. IEEE Access 2015;3:1011–1025. http://ieeexplore.ieee.org/document/7140733/.CrossRefGoogle Scholar
  38. 38.
    Tang J, Deng C, Huang Gb. IEEE Transactions on Neural Networks and Learning Systems 2016;27(4):809.  https://doi.org/10.1109/TNNLS.2015.2424995. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7103337.MathSciNetCrossRefGoogle Scholar
  39. 39.
    Anam K, Al-jumaily A. A novel extreme learning machine for dimensionality reduction on finger movement classification using sEMG. 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE; 2015. p. 824–827,  https://doi.org/10.1109/NER.2015.7146750. http://ieeexplore.ieee.org/document/7146750/.
  40. 40.
    Park MS, Kim K, Oh SR. A fast classification system for decoding of human hand configurations using multi-channel sEMG signals. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2011. p. 4483–4487.  https://doi.org/10.1109/IROS.2011.6048805. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6048805.
  41. 41.
    Lee H, Kim SJ, Kim K, Park MS, Kim SK, Park JH, Oh SR. 2011. In: IEEE international conference on robotics and biomimetics, ROBIO 2011, pp 2243–2244.  https://doi.org/10.1109/ROBIO.2011.6181628.
  42. 42.
    Kim M, Kim K. 2013. (ICCAS) 15.Google Scholar
  43. 43.
    Anam K, Al-Jumaily A. 2014. In: Middle east conference on biomedical engineering, MECBME, pp 273–276.  https://doi.org/10.1109/MECBME.2014.6783257.
  44. 44.
    Cao J, Lin Z. Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey. Math Probl Eng 2015;2015:1–13. http://www.hindawi.com/journals/mpe/2015/103796/.zbMATHGoogle Scholar
  45. 45.
    Scardapane S, Comminiello D, Scarpiniti M, Uncini A. IEEE Transactions on Neural Networks and Learning Systems 2015;26(9):2214.  https://doi.org/10.1109/TNNLS.2014.2382094. http://www.ncbi.nlm.nih.gov/pubmed/25561597.MathSciNetCrossRefGoogle Scholar
  46. 46.
    Miche Y, Sorjamaa A, Lendasse A. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2008;5163 LNCS(PART 1):145.  https://doi.org/10.1007/978-3-540-87536-9_16.Google Scholar
  47. 47.
    Parente Mesquita D, Gomes J, Ramos Rodrigues L, Kawakami Galvao R. IEEE Lat Am Trans 2015; 13(12):3974.  https://doi.org/10.1109/TLA.2015.7404935.CrossRefGoogle Scholar
  48. 48.
    Cene VH, Favieiro G, Balbinot A. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Jeju: IEEE; 2017. p. 2047–2050.  https://doi.org/10.1109/EMBC.2017.8037255. http://ieeexplore.ieee.org/document/8037255/.
  49. 49.
    Phinyomark A, Quaine F, Charbonnier S, Serviere C, Tarpin-Bernard F, Laurillau Y. Expert Syst Appl 2013;40(12):4832.  https://doi.org/10.1016/j.eswa.2013.02.023.CrossRefGoogle Scholar
  50. 50.
    Englehart K, Hudgins B. IEEE Transactions on Bio-medical Engineering 2003; 50(7):848.  https://doi.org/10.1109/TBME.2003.813539.CrossRefGoogle Scholar
  51. 51.
    Farrell TR, Weir RF. IEEE Trans Neural Syst Rehabil Eng 2007;15(1):111.  https://doi.org/10.1109/TNSRE.2007.891391. http://ieeexplore.ieee.org/document/4126535/.CrossRefGoogle Scholar

Copyright information

© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Graduate Program of Electrical Engineering (PPGEE), Department of Electrical Engineering, Laboratory of Electro-Electronic Instrumentation (IEE)Universidade Federal do Rio Grande do Sul (UFRGS)Porto AlegreBrazil

Personalised recommendations