Skip to main content
Top
Published in: Neural Computing and Applications 8/2019

20-11-2017 | Original Article

A new hand finger movements’ classification system based on bicoherence analysis of two-channel surface EMG signals

Author: Necmettin Sezgin

Published in: Neural Computing and Applications | Issue 8/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this study, two-channel surface electromyography (sEMG) signals were used to classify hand finger movements. Bicoherence analysis of the sEMG signal recorded with surface electrodes for flexor and extensor muscle bundles on the front and back of the forearm, respectively, was classified by extreme learning machines (ELM) based on phase matches in the electromyography (EMG) signal. EMG signals belonging to 42 human, 22 males and 20 females, with an average age of 21.4 were used in the study. The finger movements were also classified by using different learning algorithms. The best classification was performed by using ELM algorithm with 98.95 and 97.83% accuracies in average for subjects individually and all together, respectively. On the other hand, a maximum of 95.81 and 94.30% accuracies were reached for subjects individually and all together, respectively, with other learning methods used in the present study. From the information obtained through this study, it is possible to control finger movements by using flexor and extensor muscle activities of the forearm. Furthermore, by this method, it may be possible controlling of the intelligent prosthesis hands with high degree of freedom.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Oskoei MA, Hu H (2007) Myoelectric control systems—a survey. Biomed Signal Process Control 2(4):275–294CrossRef Oskoei MA, Hu H (2007) Myoelectric control systems—a survey. Biomed Signal Process Control 2(4):275–294CrossRef
2.
go back to reference Hudgins B, Parker PA, Scott RN (1993) A new strategy for multifunction myoelectric control. IEEE Trnas Biomed Eng 40:82–94CrossRef Hudgins B, Parker PA, Scott RN (1993) A new strategy for multifunction myoelectric control. IEEE Trnas Biomed Eng 40:82–94CrossRef
3.
go back to reference Englehart K, Hudgins B, Parker PA (2001) A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans Biomed Eng 48:302–311CrossRef Englehart K, Hudgins B, Parker PA (2001) A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans Biomed Eng 48:302–311CrossRef
4.
go back to reference Karlik B, Tokhi MO, Alci M (2003) A fuzzy clustering neural network architecture for multifunction upper-limb prostheses. IEEE Trans Biomed Eng 50:1255–1261CrossRef Karlik B, Tokhi MO, Alci M (2003) A fuzzy clustering neural network architecture for multifunction upper-limb prostheses. IEEE Trans Biomed Eng 50:1255–1261CrossRef
5.
go back to reference Mahdi K, Mehran J (2007) A novel approach to recognize hand movements via sEMG patterns. In: Engineering in medicine and biology society, 29th annual international conference of the IEEE Mahdi K, Mehran J (2007) A novel approach to recognize hand movements via sEMG patterns. In: Engineering in medicine and biology society, 29th annual international conference of the IEEE
6.
go back to reference Zhizeng L, Xiaoliang R, Yutao Z (2004) Multi-pattern recognition of the forearm movement based on SEMG. In: International conference on information acquisition, IEEE Zhizeng L, Xiaoliang R, Yutao Z (2004) Multi-pattern recognition of the forearm movement based on SEMG. In: International conference on information acquisition, IEEE
7.
go back to reference Engin EZ, Taşan D, Engin M (2015) Çok İşlevli Protez El Kontrolü İçin Önkol Elektromiyografi İşaretlerinin Sınıflandırılması. Dokuz Eylül Üniversitesi Fen ve Mühendislik Dergisi 17(1):35–46 Engin EZ, Taşan D, Engin M (2015) Çok İşlevli Protez El Kontrolü İçin Önkol Elektromiyografi İşaretlerinin Sınıflandırılması. Dokuz Eylül Üniversitesi Fen ve Mühendislik Dergisi 17(1):35–46
8.
go back to reference Zeghbib A, Palis F, Ben-Ouezdou F (2005) EMG-based finger movement classification using transparent fuzzy system. In: EUSFLAT-LFA, pp 816–821 Zeghbib A, Palis F, Ben-Ouezdou F (2005) EMG-based finger movement classification using transparent fuzzy system. In: EUSFLAT-LFA, pp 816–821
9.
go back to reference Khushaba RN, Kodagoda S, Takruri M, Dissanayake G (2012) Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Syst Appl 39:10731–10738CrossRef Khushaba RN, Kodagoda S, Takruri M, Dissanayake G (2012) Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Syst Appl 39:10731–10738CrossRef
10.
go back to reference Oskoei MA, Hu H (2008) Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans Biomed Eng 55:1956–1965CrossRef Oskoei MA, Hu H (2008) Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans Biomed Eng 55:1956–1965CrossRef
11.
go back to reference Al-Timemy AH, Bugmann G, Escudero J, Outram N (2013) Classification of finger movements for the dexterous hand prosthesis control with surface electromyography. IEEE J Biomed Health Inform 17:608–618CrossRef Al-Timemy AH, Bugmann G, Escudero J, Outram N (2013) Classification of finger movements for the dexterous hand prosthesis control with surface electromyography. IEEE J Biomed Health Inform 17:608–618CrossRef
12.
go back to reference Park MS, Kim K, Oh SR (2011) A fast classification system for decoding of human hand configurations using multi-channel sEMG signals. In: IEEE international workshop on intelligent robots and systems, IROS, pp 4483–4487 Park MS, Kim K, Oh SR (2011) A fast classification system for decoding of human hand configurations using multi-channel sEMG signals. In: IEEE international workshop on intelligent robots and systems, IROS, pp 4483–4487
13.
go back to reference Lee H, Kim S-J, Kim K, Park MS, Kim S-K, Parfk JH et al (2011) Online remote control of a robotic hand configurations using sEMG signals on a forearm. In: The IEEE international conference on robotics and biomimetics, ROBIO (pp 2243–2244) Lee H, Kim S-J, Kim K, Park MS, Kim S-K, Parfk JH et al (2011) Online remote control of a robotic hand configurations using sEMG signals on a forearm. In: The IEEE international conference on robotics and biomimetics, ROBIO (pp 2243–2244)
14.
go back to reference Shi J, Cai Y, Zhu J, Zhong J, Wang F (2013) SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine. Med Biol Eng Comput 51:417–427CrossRef Shi J, Cai Y, Zhu J, Zhong J, Wang F (2013) SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine. Med Biol Eng Comput 51:417–427CrossRef
15.
go back to reference Anam K, Al-Jumaily A (2017) Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees. Neural Netw 85:51–68CrossRef Anam K, Al-Jumaily A (2017) Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees. Neural Netw 85:51–68CrossRef
16.
go back to reference Mane SM, Kambli RA, Kazi FS, Singh NM (2015) Hand motion recognition from single channel surface EMG using wavelet and artificial neural network. Proc Comput Sci 49:58–65CrossRef Mane SM, Kambli RA, Kazi FS, Singh NM (2015) Hand motion recognition from single channel surface EMG using wavelet and artificial neural network. Proc Comput Sci 49:58–65CrossRef
17.
go back to reference Khushaba RN, Al-Timemy A, Kodagoda S, Nazarpour K (2016) Combined influence of forearm orientation and muscular contraction on EMG pattern recognition. Expert Syst Appl 61:154–161CrossRef Khushaba RN, Al-Timemy A, Kodagoda S, Nazarpour K (2016) Combined influence of forearm orientation and muscular contraction on EMG pattern recognition. Expert Syst Appl 61:154–161CrossRef
18.
go back to reference Tavakoli M, Benussi C, Lourenco JL (2017) Single channel surface EMG control of advanced prosthetic hands: a simple, low cost and efficient approach. Expert Syst Appl 79:322–332CrossRef Tavakoli M, Benussi C, Lourenco JL (2017) Single channel surface EMG control of advanced prosthetic hands: a simple, low cost and efficient approach. Expert Syst Appl 79:322–332CrossRef
19.
go back to reference Al-Angari HM, Kanitz G, Tarantino S, Cipriani C (2016) Distance and mutual information methods for EMG feature and channel subset selection for classification of hand movements. Biomed Signal Process Control 27:24–31CrossRef Al-Angari HM, Kanitz G, Tarantino S, Cipriani C (2016) Distance and mutual information methods for EMG feature and channel subset selection for classification of hand movements. Biomed Signal Process Control 27:24–31CrossRef
20.
go back to reference Chua KC, Chandran V, Acharya UR, Lim CM (2010) Application of higher order statistics/spectra in biomedical signals—a review. Med Eng Phys 32:679–689CrossRef Chua KC, Chandran V, Acharya UR, Lim CM (2010) Application of higher order statistics/spectra in biomedical signals—a review. Med Eng Phys 32:679–689CrossRef
22.
go back to reference Martis RJ, Acharya R, Mandana KM, Ray AK, Chakraborty C (2013) Cardiac decision making using higher order spectra. Biomed Signal Process Control 8:193–203CrossRef Martis RJ, Acharya R, Mandana KM, Ray AK, Chakraborty C (2013) Cardiac decision making using higher order spectra. Biomed Signal Process Control 8:193–203CrossRef
23.
go back to reference Martis RJ, Acharya UR, Ray AK, Chakraborty C (2011) Application of higher order cumulants to ECG signals for the cardiac health diagnosis. In: 33rd Annual international conference of the IEEE EMBS Boston, Massachusetts USA, August 30–September 3, 2011 Martis RJ, Acharya UR, Ray AK, Chakraborty C (2011) Application of higher order cumulants to ECG signals for the cardiac health diagnosis. In: 33rd Annual international conference of the IEEE EMBS Boston, Massachusetts USA, August 30–September 3, 2011
26.
go back to reference Bilodeau M, Cincera M, Arsenault A, Gravel D (1997) Normality and stationarity of EMG signals of elbow flexor muscles during ramp and step isometric contractions. J Electromyogr Kinesiol 7(2):87–96CrossRef Bilodeau M, Cincera M, Arsenault A, Gravel D (1997) Normality and stationarity of EMG signals of elbow flexor muscles during ramp and step isometric contractions. J Electromyogr Kinesiol 7(2):87–96CrossRef
27.
go back to reference Zazula D (2001) Experience with surface EMG decomposition using higher-order cumulants. In: Signal processing. Poznań, Italy, pp 19–24 Zazula D (2001) Experience with surface EMG decomposition using higher-order cumulants. In: Signal processing. Poznań, Italy, pp 19–24
28.
go back to reference Hinich MJ, Clay CS (1968) The application of the discrete fourier transform in the estimation of power spectra, coherence and bispectra of geophysical data. Rev Geophys 6(3):347–363CrossRef Hinich MJ, Clay CS (1968) The application of the discrete fourier transform in the estimation of power spectra, coherence and bispectra of geophysical data. Rev Geophys 6(3):347–363CrossRef
29.
go back to reference Sezgin N (2016) Epileptik EEG işaretlerin aşırı öğrenme makineleri ile sınıflandırılması. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 7(3):481–490 Sezgin N (2016) Epileptik EEG işaretlerin aşırı öğrenme makineleri ile sınıflandırılması. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 7(3):481–490
30.
go back to reference Sezgin N,Tagluk ME, Akin M (2010) Using bispectral analysis in OSAS estimation. In: IEEE 18th signal processing and communications applications conference, pp 89–92, 22–24 April, Diyarbakır Sezgin N,Tagluk ME, Akin M (2010) Using bispectral analysis in OSAS estimation. In: IEEE 18th signal processing and communications applications conference, pp 89–92, 22–24 April, Diyarbakır
32.
go back to reference Raghuveer MR, Nikias CL (1985) (1985), Bispectrum estimation: a parametric approach. IEEE Trans Acoust Speech Signal Process 33:1113–1230CrossRef Raghuveer MR, Nikias CL (1985) (1985), Bispectrum estimation: a parametric approach. IEEE Trans Acoust Speech Signal Process 33:1113–1230CrossRef
33.
go back to reference Nikias CL, Petropulu AP (1993) Higher order spectral analysis: a nonlinear signal processing framework. Prentice-Hall, Engle-wood CliffsCrossRefMATH Nikias CL, Petropulu AP (1993) Higher order spectral analysis: a nonlinear signal processing framework. Prentice-Hall, Engle-wood CliffsCrossRefMATH
34.
go back to reference Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501CrossRef Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501CrossRef
35.
go back to reference Suresh S, Saraswathi S, Sundararajan N (2010) Performance enhancement of extreme learning machine for multi-category sparse data classification problems. Eng Appl Artif Intell 23:1149–1157CrossRef Suresh S, Saraswathi S, Sundararajan N (2010) Performance enhancement of extreme learning machine for multi-category sparse data classification problems. Eng Appl Artif Intell 23:1149–1157CrossRef
36.
go back to reference Ertugrul OF, Tagluk ME, Kaya Y, Tekin R (2013) EMG signal classification by extreme learning machine. In: IEEE signal processing and communications applications conference, 24–26 April, Northern Cyprus Ertugrul OF, Tagluk ME, Kaya Y, Tekin R (2013) EMG signal classification by extreme learning machine. In: IEEE signal processing and communications applications conference, 24–26 April, Northern Cyprus
37.
go back to reference Ertuğrul ÖF, Tağluk ME (2017) A fast feature selection approach based on extreme learning machine and coefficient of variation. Turk J Electr Eng Comput Sci 25:3409–3420CrossRef Ertuğrul ÖF, Tağluk ME (2017) A fast feature selection approach based on extreme learning machine and coefficient of variation. Turk J Electr Eng Comput Sci 25:3409–3420CrossRef
38.
go back to reference Tenore FVG, Ramos A, Fahmy A, Acharya S, Etienne-Cummings R, Thakor NV (2009) Decoding of individuated finger movements using surface electromyography. IEEE Trans Biomed Eng 56:1427–1434CrossRef Tenore FVG, Ramos A, Fahmy A, Acharya S, Etienne-Cummings R, Thakor NV (2009) Decoding of individuated finger movements using surface electromyography. IEEE Trans Biomed Eng 56:1427–1434CrossRef
39.
go back to reference Cipriani C, Antfolk C, Controzzi M, Lundborg G, Rosen B, Carrozza MC, Sebelius F (2011) Online myoelectric control of a dexterous hand prosthesis by transradial amputees. IEEE Trans Neural Syst Rehabil Eng 19:260–270CrossRef Cipriani C, Antfolk C, Controzzi M, Lundborg G, Rosen B, Carrozza MC, Sebelius F (2011) Online myoelectric control of a dexterous hand prosthesis by transradial amputees. IEEE Trans Neural Syst Rehabil Eng 19:260–270CrossRef
Metadata
Title
A new hand finger movements’ classification system based on bicoherence analysis of two-channel surface EMG signals
Author
Necmettin Sezgin
Publication date
20-11-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 8/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3286-z

Other articles of this Issue 8/2019

Neural Computing and Applications 8/2019 Go to the issue

Premium Partner