Skip to main content
SpringerLink
Log in

Surface EMG signals and deep transfer learning-based physical action classification

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Human physical action classification is an emerging area of research for human-to-machine interaction, which can help to disable people to interact with real world, and robotics application. EMG signals measure the electrical activity muscular systems, which involved in physical action of human. EMG signals provide more information related to physical action. In this paper, we proposed deep transfer learning-based approach of human action classification using surface EMG signals. The surface EMG signals are represented by time–frequency image (TFI) by using short-time Fourier transform. TFI is used as input to pre-trained convolutional neural network models, namely AlexNet and VGG16, for deep feature extraction, and support vector machine (SVM) classifier is used for classification of physical action of EMG signals. Also, the fine-tuning of the pre-trained AlexNet model is also considered. The experimental results show that deep feature extraction and SVM classification method and fine-tuning have obviously improved the classification accuracy when compared with various results from the literature. The 99.04% accuracy score is obtained with AlexNet fc6 + AlexNet fc7 + VGG16 fc6 + VGG16 fc7 deep feature concatenation and SVM classification. 98.65% accuracy score is performed by fine-tuning of the AlexNet model. We also compare the obtained results with some of the existing methods. The comparisons show that the deep feature concatenation and SVM classification method provide better classification accuracy than the compared methods.

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

Similar content being viewed by others

References

  1. Durandau G, Sartori M, Bortole M, Moreno JC, Pons JL, Farina D (2016) EMG-driven models of human–machine interaction in individuals wearing the H2 exoskeleton. IFAC-PapersOnLine 49(32):200–203

    Article  Google Scholar 

  2. Furui A, Hayashi H, Nakamura G, Chin T, Tsuji T (2017) An artificial EMG generation model based on signal-dependent noise and related application to motion classification. PLoS ONE 12(6):e0180112. https://doi.org/10.1371/journal.pone.0180112

    Article  Google Scholar 

  3. Sezgin N (2012) Analysis of EMG signals in aggressive and normal activities by using higher-order spectra. Sci World J, Volume 2012, Article ID 478952, 5 pages. https://doi.org/10.1100/2012/478952

    Article  Google Scholar 

  4. Wang L, Ge KD, Wu JY, Ye Y, Wei W (2018) A novel approach for the pattern recognition of hand movements based on EMG and VPMCD. J Mech Med Biol 18(01):1750115

    Article  Google Scholar 

  5. Podrug E, Subasi A (2015) Surface EMG pattern recognition by using DWT feature extraction and SVM classifier. In The 1st conference of medical and biological engineering in Bosnia and Herzegovina (CMBEBIH 2015), 13–15 March 2015, Sarajevo

  6. Li G (2011) Electromyography pattern-recognition-based control of powered multifunctional upper-limb prostheses. In: Mizrahi J (ed) Advances in applied electromyography. InTech, pp 99–116

  7. Gokgoz E, Subasi A (2015) Comparison of decision tree algorithms for EMG signal classification using DWT. Biomed Signal Process Control 18:138–144

    Article  Google Scholar 

  8. Sahu GN, Chaurasia N, Suwalka PP, Bajaj V, Kumar A (2015) HHT based features for discrimination of EMG signals. Information systems design and intelligent applications. Springer, Berlin, pp 95–103

    Chapter  Google Scholar 

  9. Bajaj V, Kumar A (2015) Features based on intrinsic mode functions for classification of EMG signals. Int J Biomed Eng Technol 18(2):156–167

    Article  Google Scholar 

  10. Mishra VK, Bajaj V, Kumar A, Sharma D, Singh GK (2017) An efficient method for analysis of EMG signals using improved empirical mode decomposition. AEU-Int. J. Electron. Commun. 72:200–209

    Article  Google Scholar 

  11. Sahinbegovic H, Music L, and Alic B (2017) Distinguishing physical actions using an artificial neural network. In: 26th International conference on information, communication and automation technologies, pp 1– 5

  12. Jana GC, Swetapadma A, and Pattnaik P (2017) An intelligent method for classification of normal and aggressive actions from electromyography signals. In: 1st International conference on electronics, materials engineering and nano-technology, pp 1–5

  13. Abdullah AA, Subasi A, Qaisar SM (2017) Surface EMG signal classification by using WPD and ensemble tree classifiers. In: CMBEBIH. Springer, pp 475–481

  14. Sukumar N, Taran S, Bajaj V (2018) Physical actions classification of surface EMG signals using VMD. In: International conference on communication and signal processing. April 3–5, India, pp 715–719

  15. Krizhevsky A, Sutskever I, and Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  16. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Computing Research Repository (CoRR), vol. arXiv:1409.1556

  17. Orenstein EC, Oscar B (2017) Transfer learning and deep feature extraction for planktonic image data Sets. In: 2017 IEEE winter conference on applications of computer vision (WACV). IEEE

  18. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115(3):211–252

    Article  MathSciNet  Google Scholar 

  19. https://archive.ics.uci.edu/ml/datasets/EMG+Physical+Action+Data+Set

  20. Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst Appl 13(4):18–28

    Article  Google Scholar 

  21. Vedaldi A, Zisserman A (2012) Efficient additive kernels via explicit feature maps. IEEE Trans Pattern Anal Mach Intell 34(3):480–492

    Article  Google Scholar 

  22. He K, Zhang X, Ren S, and Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  23. BVLC_GoogLeNet_Model. https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet

  24. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  25. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50× fewer parameters and < 0.5 MB model size. arXiv:1602.07360

  26. ImageNet. http://www.image-net.org

Download references

Author information

Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Technology Faculty, Firat University, Elazig, Turkey

    Fatih Demir, Melih C. Ince & Abdulkadir Şengür

  2. Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India

    Varun Bajaj & Sachin Taran

Authors
  1. Fatih Demir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Varun Bajaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Melih C. Ince
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sachin Taran
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abdulkadir Şengür
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Varun Bajaj.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Demir, F., Bajaj, V., Ince, M.C. et al. Surface EMG signals and deep transfer learning-based physical action classification. Neural Comput & Applic 31, 8455–8462 (2019). https://doi.org/10.1007/s00521-019-04553-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04553-7

Keywords

Search

Navigation