Skip to main content
Springer Nature Link
Log in

Surface EMG signal classification using TQWT, Bagging and Boosting for hand movement recognition

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Hands play a significant role in grasping and manipulating different objects. The loss of even a single hand have impact on the human activity. In this regard, a prosthetic hand is an appealing solution for the subjects who lost their hands. The surface electromyogram (sEMG) plays a vital role in the design of prosthesis hands. The ensemble classifiers achieve better performance by using a weighted combination of several classifier models. Hence, in this paper, the feasibility of the Bagging and the Boosting ensemble classifiers is assessed for the basic hand movement recognition by using sEMG signals, which were recorded during the grasping movements with various objects for the six hand motions. So, the novelty of the current study is the development of an ensemble model for hand movement recognition based on the tunable Q-factor wavelet transform (TQWT). The proposed method consists of three steps. In the first step, MSPCA is used for denoising. In the second step, a novel feature extraction method, TQWT is used for feature extraction from the sEMG signals, then, statistical values of TQWT sub-bands are calculated. In the last step, the obtained feature set is used as input to an ensemble classifier for the identification of intended hand movements. Performances of the Bagging and the Boosting ensemble classifiers are compared in terms of different performance measures. Using TQWT extracted features along with the presented the Adaboost with SVM and the Multiboost with SVM classifier results in a classification accuracy up to 100%. Hence, the results have shown that the proposed framework has achieved overall better performance and it is a potential candidate for the prosthetic hands control.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. https://archive.ics.uci.edu/ml/datasets/sEMG+for+Basic+Hand+movements#.

Abbreviations

sEMG:

Surface electromyography

MMI:

Man–machine interaction

HMR:

Hand movement recognition

MSPCA:

Multi-scale Principle Component Analysis

TQWT:

Tunable Q- factor wavelet transform

DWT:

Discrete Wavelet Transform

SVM:

Support Vector Machine

k-NN:

K-Nearest Neighbors

NB:

Naive Bayes

ANN:

Artificial neural network

References

  • AbdelMaseeh M, Chen T-W, Stashuk DW (2016) Extraction and classification of multichannel electromyographic activation trajectories for hand movement recognition. IEEE Trans Neural Syst Rehabil Eng 24(6):662–673

    Google Scholar 

  • Abdullah AA, Subasi A, Qaisar SM (2017) Surface EMG signal classification by using WPD and ensemble tree classifiers. In: Badnjevic A (ed) CMBEBIH 2017. IFMBE proceedings, vol 62. Springer, Singapore

    Google Scholar 

  • Ahsan MR, Ibrahimy MI, Khalifa OO (2009) EMG signal classification for human computer interaction: a review. Eur J Sci Res 33(3):480–501

    Google Scholar 

  • AlOmari F, Liu G (2014) Analysis of extracted forearm sEMG signal using LDA, QDA, K-NN classification algorithms. Open Autom Control Syst J 6:108–116

    Google Scholar 

  • AlOmari F, Liu G (2015) Novel hybrid soft computing pattern recognition system SVM–GAPSO for classification of eight different hand motions. Optik 126(23):4757–4762. https://doi.org/10.1016/j.ijleo.2015.08.170

    Article  Google Scholar 

  • Alpaydin E (2014) Introduction to machine learning. MIT press, Cambridge

    MATH  Google Scholar 

  • Asadi H, Kaboli S, Oladazimi M, Safari M (2011) A review on Li-ion battery charger techniques and optimize battery charger performance by fuzzy logic. ICICA 18(201):89–96

    Google Scholar 

  • Bakshi BR (1998) Multiscale PCA with application to multivariate statistical process monitoring. AIChE J 44(7):1596–1610

    Google Scholar 

  • Bauer E, Kohavi R (1999) An empirical comparison of voting classification algorithms: Bagging, Boosting, and variants. Mach Learn 36(1):105–139

    Google Scholar 

  • Blankertz B, Muller K-R, Krusienski DJ, Schalk G, Wolpaw JR, Schlogl A, Pfurtscheller G, Millan JR, Schroder M, Birbaumer N (2006) The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng 14(2):153–159

    Google Scholar 

  • Boyali A, Hashimoto N (2016) Spectral collaborative representation based classification for hand gestures recognition on electromyography signals. Biomed Signal Process Control 24:11–18. https://doi.org/10.1016/j.bspc.2015.09.001

    Article  Google Scholar 

  • Brown G (2011) Ensemble learning. In: Encyclopedia of machine learning. Springer, pp. 312–320.

  • Chowdhury RH, Reaz MB, Ali MABM, Bakar AA, Chellappan K, Chang TG (2013) Surface electromyography signal processing and classification techniques. Sensors 13(9):12431–12466

    Google Scholar 

  • Coelho AL, Lima CA (2014) Assessing fractal dimension methods as feature extractors for EMG signal classification. Eng Appl Artif Intell 36:81–98

    Google Scholar 

  • El Dabbagh H, Fakhr W (2011) Multiple classification algorithms for the BCI P300 speller diagram using ensemble of SVMs. In: 2011 IEEE GCC conference and exhibition (GCC), Dubai, pp 393–396.

  • Daubechies I (1992) Ten lectures on wavelets. SIAM, Philadelphia

    MATH  Google Scholar 

  • Daumé H III (2012) A course in machine learning. Chapter 5:69

    Google Scholar 

  • Dong C, Lin Y (2005) Development of virtual testing instrument based on LabVIEW and DAQ─ PCI-4472 [J]. China Meas Technol 3.

  • Gicić A, Subasi A (2019) Credit scoring for a microcredit data set using the synthetic minority oversampling technique and ensemble classifiers. Expert Syst 36(2):e12363

    Google Scholar 

  • Guo W, Sheng X, Liu H, Zhu X (2017) Toward an enhanced human–machine interface for upper-limb prosthesis control with combined EMG and NIRS signals. IEEE Trans Hum-Mach Syst 47(4):564–575

    Google Scholar 

  • Hall M, Witten I, Frank E (2011) Data mining: practical machine learning tools and techniques. Kaufmann, Burlington

    Google Scholar 

  • Ju Z, Ouyang G, Wilamowska-Korsak M, Liu H (2013) Surface EMG based hand manipulation identification via nonlinear feature extraction and classification. IEEE Sens J 13(9):3302–3311

    Google Scholar 

  • Karimi M, Pourghassem H, Shahgholian G (2011) A novel prosthetic hand control approach based on genetic algorithm and wavelet transform features. In: 2011 IEEE 7th international colloquium on signal processing and its applications, Penang, pp 287–292

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

    Google Scholar 

  • Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley

  • Kurzynski M, Krysmann M, Trajdos P, Wolczowski A (2016) Multiclassifier system with hybrid learning applied to the control of bioprosthetic hand. Comput Biol Med 69:286–297. https://doi.org/10.1016/j.compbiomed.2015.04.023

    Article  Google Scholar 

  • Lee Y-R, Kim H-N (2018) A data partitioning method for increasing ensemble diversity of an eSVM-based P300 speller. Biomed Signal Process Control 39:53–63

    Google Scholar 

  • Lee S, Kim M-O, Kang T, Park J, Choi Y (2018) Knit band sensor for myoelectric control of surface EMG-based prosthetic hand. IEEE Sens J 18:8578–8586

    Google Scholar 

  • Liu Z, Luo Z (2008) Hand motion pattern classifier based on EMG using wavelet packet transform and LVQ neural networks. In: 2008 IEEE international symposium on IT in medicine and education, Xiamen, pp 28–32

  • Ma J, Thakor NV, Matsuno F (2015) Hand and wrist movement control of myoelectric prosthesis based on synergy. IEEE Trans Hum-Mach Syst 45(1):74–83

    Google Scholar 

  • Mane S, Kambli R, Kazi F, Singh N (2015) Hand motion recognition from single channel surface EMG using wavelet & artificial neural network. Procedia Comput Sci 49:58–65

    Google Scholar 

  • Matsubara T, Hyon S, Morimoto J (2011) Learning and adaptation of a stylistic myoelectric interface: EMG-based robotic control with individual user differences. In: 2011 IEEE international conference on robotics and biomimetics. Karon Beach, Phuket, pp 390–395

    Google Scholar 

  • Merletti R, Di Torino P (1999) Standards for reporting EMG data. J Electromyogr Kinesiol 9(1):3–4

    Google Scholar 

  • Oladazimi M, Vaneghi FM, Safari MJ, Asadi H, Kaboli SHA (2012) A review for feature extraction of EMG signal processing. In: Zhou J (ed) International conference on computer and automation engineering (ICCAE 2012), 4th edn. ASME Press

  • Patidar S, Pachori RB (2014) Classification of cardiac sound signals using constrained tunable-Q wavelet transform. Expert Syst Appl 41(16):7161–7170

    Google Scholar 

  • Peng Y (2006) A novel ensemble machine learning for robust microarray data classification. Comput Biol Med 36(6):553–573

    Google Scholar 

  • Phinyomark A, Limsakul C, Phukpattaranont P (2009) A novel feature extraction for robust EMG pattern recognition. ArXiv Preprint ArXiv:0912.3973.

  • Phinyomark A, Quaine F, Charbonnier S, Serviere C, Tarpin-Bernard F, Laurillau Y (2013) EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Syst Appl 40(12):4832–4840

    Google Scholar 

  • Pinnington HC, Lloyd DG, Besier TF, Dawson B (2005) Kinematic and electromyography analysis of submaximal differences running on a firm surface compared with soft, dry sand. Eur J Appl Physiol 94(3):242–253

    Google Scholar 

  • Purushothaman G, Ray K (2014) EMG based man–machine interaction—a pattern recognition research platform. Robot Auton Syst 62(6):864–870

    Google Scholar 

  • Rafiee J, Rafiee M, Yavari F, Schoen M (2011) Feature extraction of forearm EMG signals for prosthetics. Expert Syst Appl 38(4):4058–4067

    Google Scholar 

  • Rakotomamonjy A, Guigue V (2008) BCI competition III: dataset II-ensemble of SVMs for BCI P300 speller. IEEE Trans Biomed Eng 55(3):1147–1154

    Google Scholar 

  • Rechy-Ramirez EJ, Hu H (2015) Bio-signal based control in assistive robots: a survey. Digit Commun Netw 1(2):85–101. https://doi.org/10.1016/j.dcan.2015.02.004

    Article  Google Scholar 

  • Robertson DGE, Caldwell GE, Hamill J, Kamen G, Whittlesey SN (2013) Research methods in biomechanics, 2nd edn. Human Kinetics, 475 Devonshire Road Unit 100, Windsor, ON N8Y 2L5

  • Saha I, Zubek J, Klingström T, Forsberg S, Wikander J, Kierczak M, Maulik U, Plewczynski D (2014) Ensemble learning prediction of protein–protein interactions using proteins functional annotations. Mol BioSyst 10(4):820–830

    Google Scholar 

  • Sapsanis C, Georgoulas G, Tzes A (2013a) EMG based classification of basic hand movements based on time-frequency features. In: 21st mediterranean conference on control and automation, Chania, pp 716–722

  • Sapsanis C, Georgoulas G, Tzes A, Lymberopoulos D (2013b) Improving EMG based classification of basic hand movements using EMD. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 5754–5757

  • Saraswathi D, Srinivasan E (2014) An ensemble approach to diagnose breast cancer using fully complex-valued relaxation neural network classifier. Int J Biomed Eng Technol 15(3):243–260

    Google Scholar 

  • Selesnick I (2011a) TQWT toolbox guide. Electrical and computer engineering, Polytechnic Institute of New York University. Available Online at: http://eeweb.poly.edu/iselesni/TQWT/TQWT_guide.pdf.

  • Selesnick IW (2011b) Wavelet transform with tunable Q-factor. IEEE Trans Signal Process 59(8):3560–3575

    MathSciNet  MATH  Google Scholar 

  • da Silva-Sauer L, Valero-Aguayo L, de la Torre-Luque A, Ron-Angevin R, Varona-Moya S (2016) Concentration on performance with P300-based BCI systems: a matter of interface features. Appl Ergon 52:325–332. https://doi.org/10.1016/j.apergo.2015.08.002

    Article  Google Scholar 

  • Skurichina M, Duin RP (2002) Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal Appl 5(2):121–135

    MathSciNet  MATH  Google Scholar 

  • Stango A, Negro F, Farina D (2015) Spatial correlation of high density EMG signals provides features robust to electrode number and shift in pattern recognition for myocontrol. IEEE Trans Neural Syst Rehabil Eng 23(2):189–198

    Google Scholar 

  • Subasi A (2012) Classification of EMG signals using combined features and soft computing techniques. Appl Soft Comput 12(8):2188–2198

    Google Scholar 

  • Subasi A, Alaskandarani A, Abubakir AA, Qaisar SM (2018a) sEMG signal classification using DWT and bagging for basic hand movements. In: 2018 21st Saudi computer society national computer conference (NCC), Riyadh, pp 1–6

  • Subasi A, Alharbi L, Madani R, Qaisar SM (2018b) Surface EMG based classification of basic hand movements using rotation forest. In: 2018 Advances in science and engineering technology international conferences (ASET), Abu Dhabi, pp 1–5

  • Subasi A, Yaman E, Somaily Y, Alynabawi HA, Alobaidi F, Altheibani S (2018c) Automated EMG signal classification for diagnosis of neuromuscular disorders using DWT and Bagging. Procedia Comput Sc 140:230–237

    Google Scholar 

  • Tsai C-F (2014) Combining cluster analysis with classifier ensembles to predict financial distress. Special Issue Inf Fus Hybrid Intell Fus Syst 16:46–58. https://doi.org/10.1016/j.inffus.2011.12.001

    Article  Google Scholar 

  • Tsai A-C, Hsieh T-H, Luh J-J, Lin T-T (2014) A comparison of upper-limb motion pattern recognition using EMG signals during dynamic and isometric muscle contractions. Biomed Signal Process Control 11:17–26

    Google Scholar 

  • Valentini G (2004) Random aggregated and bagged ensembles of SVMs: an empirical bias-variance analysis. In: Roli F, Kittler J, Windeatt T (eds) Multiple classifier systems. MCS 2004. Lecture Notes in Computer Science, vol 3077. Springer, Berlin, Heidelberg

    Google Scholar 

  • Valentini G, Dietterich TG (2004) Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods. J Mach Learn Res 5:725–775

    MathSciNet  MATH  Google Scholar 

  • Wan S, Mak M-W, Kung S-Y (2016a) Ensemble linear neighborhood propagation for predicting subchloroplast localization of multi-location proteins. J Proteome Res 15(12):4755–4762

    Google Scholar 

  • Wan S, Mak M-W, Kung S-Y (2016) Transductive learning for multi-label protein subchloroplast localization prediction. IEEE/ACM Trans Comput Biol Bioinf 14(1):212–224

    Google Scholar 

  • Wang G, Yan Z, Hu X, Xie H, Wang Z (2006) Classification of surface EMG signals using harmonic wavelet packet transform. Physiol Meas 27(12):1255

    Google Scholar 

  • Webb GI (2000) Multiboosting: a technique for combining boosting and wagging. Mach Learn 40(2):159–196

    Google Scholar 

  • Wojtczak P, Amaral TG, Dias OP, Wolczowski A, Kurzynski M (2009) Hand movement recognition based on biosignal analysis. Eng Appl Artif Intell 22(4):608–615

    Google Scholar 

  • Xing K, Yang P, Huang J, Wang Y, Zhu Q (2014) A real-time EMG pattern recognition method for virtual myoelectric hand control. Neurocomputing 136:345–355

    Google Scholar 

  • Young AJ, Smith LH, Rouse EJ, Hargrove LJ (2013) Classification of simultaneous movements using surface EMG pattern recognition. IEEE Trans Biomed Eng 60(5):1250–1258

    Google Scholar 

  • Zhang Z, Sup F (2014) Activity recognition of the torso based on surface electromyography for exoskeleton control. Biomed Signal Process Control 10:281–288. https://doi.org/10.1016/j.bspc.2013.10.002

    Article  Google Scholar 

  • Zhang Y, Wang G, Teng C, Sun Z, Wang J (2014) The analysis of hand movement distinction based on relative frequency band energy method. BioMed Res Int 2014:781769. https://doi.org/10.1155/2014/781769

    Article  Google Scholar 

Download references

Funding

This work was supported by Effat University with the Decision Number of UC#7/28 Feb. 2018/10.2-44i, Jeddah, Saudi Arabia.

Author information

Authors and Affiliations

  1. College of Engineering, Effat University, Jeddah, 21478, Saudi Arabia

    Abdulhamit Subasi & Saeed Mian Qaisar

Authors
  1. Abdulhamit Subasi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Saeed Mian Qaisar
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Abdulhamit Subasi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Subasi, A., Qaisar, S.M. Surface EMG signal classification using TQWT, Bagging and Boosting for hand movement recognition. J Ambient Intell Human Comput 13, 3539–3554 (2022). https://doi.org/10.1007/s12652-020-01980-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-01980-6

Keywords