Skip to main content
SpringerLink
Log in

Modified EMG-based handgrip force prediction using extreme learning machine

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Various myoelectric prostheses controlled by electromyography (EMG) signals have been developed. However, there have been few studies that provide fast and accurate methods to predict handgrip force from EMG signals. Rapid and precise handgrip force prediction is required, especially for the real-time control system of myoelectric prostheses. In this study, extreme learning machine (ELM) is applied to predict handgrip force from surface EMG signals of forearm muscles. Furthermore, ELM is compared with support vector machine (SVM) and multiple nonlinear regression (MNLR). The below 10 % of the surface EMG and handgrip force signals were cut away, and then the root mean square feature extracted from the modified surface EMG signals was taken as input vector for these three kinds of predicting mechanisms. For the testing dataset, ELM achieved a slightly larger root mean squared error than SVM did and a smaller one than MNLR did. Meanwhile, all three methods showed high correlation coefficients. For the total processing time, ELM and MNLR consumed much less time than SVM did. Experimental results demonstrate that ELM possesses a relatively good accuracy and little consumed time, although SVM is effective for handgrip force estimation in terms of accuracy. Overall, ELM has a promising potential for predicting handgrip force rapidly and precisely.

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

Access this article

Log in via an institution

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

References

  • Andris F (2004) Biomechanics of the upper limbs: mechanics, modelling and musculoskeletal injuries. CRC Press, Boca Raton

    Google Scholar 

  • Chen Y, Zhao Z, Wang S, Chen Z (2012) Extreme learning machine-based device displacement free activity recognition model. Soft Comput 16:1617–1625

    Article  Google Scholar 

  • Corinna C, Vladimir V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  • De Carlo LJ (1997) The use of surface electromyography in biomechanics. J Appl Biomech 13(1):135–163

    Google Scholar 

  • Emer PD, Madeleine ML, David PF, Mark JO (2008) Effect of elbow joint angle on forcecemg relationships in human elbow flexor and extensor muscles. J Electromyogr Kinesiol 18(1):760–770

    Google Scholar 

  • Eric B, Trevor H, Debashis P, Robert T (2006) Prediction by supervised principal components. J Am Stat Assoc 101(473):119–137

    Article  MathSciNet  MATH  Google Scholar 

  • Farid M, Keyvan HZ (2005) Rowing stroke force estimation with EMG signals using artificial neural networks. In: Proceedings of the 2005 IEEE conference on control applications, pp 825–830

  • Göran MH, Alwin L, Matthias J (2000) Methodologies for evaluating electromyographic field data in ergonomics. J Electromyogr Kinesiol 10:301–312

    Article  Google Scholar 

  • Haritha S, Sauvik G, Weihua S (2012) Estimation of hand force from surface electromyography signals using artificial neural network. In: Proceedings of the 10th world congress on intelligent control and automation, IEEE, pp 584–589

  • Harshvardhan V, Emken BA, Schroeder ET, Donna SM, Sukhatme GS (2013) Hierarchical linear models for energy prediction using inertial sensors: a comparative study for treadmill walking. J Ambient Intell Hum Comput 4:747–758

    Article  Google Scholar 

  • Huang G (2003) Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans Neural Netw 14(2):274–281

    Article  MathSciNet  Google Scholar 

  • Huang G, Zhu Q, Siew Ck (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  • Huang G, Wang D, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122

    Article  Google Scholar 

  • Huang G, Babri HA (1998) Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE Trans Neural Netw 9(1):224–229

  • Huang G, Zhu Q, Siew Ck (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of international joint conference on neural networks, pp 985–990

  • Ion M, Manuel G (2012) Face recognition with lattice independent component analysis and extreme learning machines. Soft Comput 16:1525–1537

    Article  Google Scholar 

  • Jakob RM, Mette FB, Ema E, Marko JN, Anne S, Ernest NK (2010) Prediction of grasping force based on features of surface and intramuscular EMG. 7th semester conference paper, pp 1–9

  • Jeremy PMM, Peter JK (2006) Prediction of forearm muscle activity during gripping. Ergonomics 49(11):1121–1130

    Article  Google Scholar 

  • Julie D, Dominique M, Jacques M (1995) Evaluation of handgrip force from EMG measurements. Appl Ergon 26(1):61–66

    Article  Google Scholar 

  • Khandoker A, Palaniswami M, Karmakar CK (2009) Support vector machines for automated recognition of obstructive sleep apnea syndrome from ECG recordings. IEEE Trans Inf Technol Biomed 13(1):37–48

    Article  Google Scholar 

  • Lee CC, Shi CY, Lai WP, Lin PC (2012) An improved boosting algorithm and its application to facial emotion recognition. J Ambient Intell Hum Comput 3:11–17

    Article  Google Scholar 

  • Marco JH, Jaap HvD (2005) Prediction of handgrip forces using surface emg of forearm muscles. J Electromyogr Kinesiol 15(4):358–366

    Article  Google Scholar 

  • Marta MC, Joaqułn LSB, Prez-Gonzlez A (2012) Hand posture prediction using neural networks within a biomechanical model. Int J Adv Robot Syst 9(139):1–11

    Google Scholar 

  • Mojgan T (2010) Analysis of hand force by EMG measurements. Simon Fraser University

  • Netta G, Allison O (2007) Human performance in a knob-turning task. In: IEEE world haptics conference, pp 96–101

  • Nor AJ, Shahrul NS, Abu US (2013) Neuro-based thumb-tip force and joint angle modelling for development of prosthetic thumb control. Int J Adv Robot Syst 10(339):1–8

    Google Scholar 

  • Pau CH, Bong-Sup P, Jeong-Su K (2011) Physiological decrements during sustained military operational stress. Mil Med 176(9):991–997

    Article  Google Scholar 

  • Peter JK, Jeremy PM (2005) The development and validation of equations to predict grip force in the workplace: contributions of muscle activity and posture. Ergonomics 48(10):1243–1259

    Article  Google Scholar 

  • Pradeep S, Kai JM, Beau C, Rajesh PNR (2008) Online electromyographic control of a robotic prosthesis. IEEE Trans Biomed Eng 55(3):1128–1135

    Article  Google Scholar 

  • Richard WB, Anneli P, Nicola MW, Johanne D, Jane BL (2006) Reference values for adult grip strength measured with a jamar dynamometer:a descriptive meta-analysis. Physiotherapy 92(1):11–15

    Article  Google Scholar 

  • Rosa-filho JS, Bemvenuti CE, Ellott M (2004) Predicting biological parameters of estuarine benthic communities using models based on environmental data. Braz Arch Biol Technol 47(4):613–627

    Article  Google Scholar 

  • Sébastien D, William B, Jean-Noël P, Frédéric G (2008) Muscular activity during uphill cycling effect of slope, posture, hand grip position and constrained bicycle lateral sways. J Electromyogr Kinesiol 18(1):116–127

    Article  Google Scholar 

  • Shahrul NS, Ahmad JHM (2012) Mapping of emg signal to hand grip force at varying wrist angles. In: 2012 IEEE EMBS international conference on biomedical engineering and sciences, pp 648–653

  • Shivajirao J, Sanjay N, Ashok G (2014) Feature elimination based random subspace ensembles learning for ECG arrhythmia diagnosis. Soft Comput 18:579–587

    Article  Google Scholar 

  • Sung PC, Hsu CC, Lee CL, Chiu PYS, Chen HL (2015) Formulating grip strength and key pinch strength prediction models for taiwanese: a comparison between stepwise regression and artificial neural networks. J Ambient Intell Hum Comput 6:37–46

  • Xie SJ, Yang J, Gong H, Yoon S, Park DS (2012) Intelligent fingerprint quality analysis using online sequential extreme learning machine. Soft Comput 16:1555–1568

    Article  Google Scholar 

  • Yang D, Zhao J, Gu Y, Jiang L, Liu H (2009) Estimation of hand grasp force based on forearm surface emg. In: Proceedings of the 2009 IEEE international conference on mechatronics and automation, pp 1795–1799

Download references

Acknowledgments

This study is partly supported by the National Natural Science Foundation of China (61303137), the Fundamental Research Funds for the Central Universities(2014QNA5009), and the Specialized Research Fund for the Doctoral Program of Higher Education(20130101110148).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Hongxin Cao, Shouqian Sun & Kejun Zhang

Authors
  1. Hongxin Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shouqian Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kejun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kejun Zhang.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, H., Sun, S. & Zhang, K. Modified EMG-based handgrip force prediction using extreme learning machine. Soft Comput 21, 491–500 (2017). https://doi.org/10.1007/s00500-015-1800-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-015-1800-8

Keywords

Search

Navigation