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Surface electromyography feature extraction via convolutional neural network

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Abstract

Although a large number of surface electromyography (sEMG) features have been proposed to improve hand gesture recognition accuracy, it is still hard to achieve acceptable performance in inter-session and inter-subject tests. To promote the application of sEMG-based human machine interaction, a convolutional neural network based feature extraction approach (CNNFeat) is proposed to improve hand gesture recognition accuracy. A sEMG database is recorded from eight subjects while performing ten hand gestures. Three classic classifiers, including linear discriminant analysis (LDA), support vector machine (SVM) and K nearest neighbor (KNN), are employed to compare the CNNFeat with 25 traditional features. This work concentrates on the analysis of CNNFeat through accuracy, safety index and repeatability index. The experimental results show that CNNFeat outperforms all the tested traditional features in inter-subject test and is listed as the best three features in inter-session test. Besides, it is also found that combining CNNFeat with traditional features can further improve the accuracy by 4.35%, 3.62% and 4.7% for SVM, LDA and KNN, respectively. Additionally, this work also demonstrates that CNNFeat can be potentially enhanced with more data for model training.

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Notes

  1. https://github.com/taowucheng1026/CNN-LDA-SVM-KNN-for-EMG.

References

  1. Atzori M, Gijsberts A, Kuzborskij I, Elsig S, Hager AG, Deriaz O, Castellini C, Muller H, Caputo B (2015) Characterization of a benchmark database for myoelectric movement classification. IEEE Trans Neural Syst Rehabil Eng 23(1):73–83

    Article  Google Scholar 

  2. Xu Z, Tian Y, Li Y (2015) sEMG pattern recognition of muscle force of upper arm for intelligent bionic limb control. J Bionic Eng 12(2):316–323

    Article  Google Scholar 

  3. Altimemy 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(3):608–618

    Article  Google Scholar 

  4. Feng Z, Li P, Hou Z, Zeng L, Chen Y, Li Q, Min T (2012) sEMG-based continuous estimation of joint angles of human legs by using BP neural network. Neurocomputing 78(1):139–148

    Article  Google Scholar 

  5. White MM, Zhang W, Winslow AT, Zahabi M, Fan Z, He H, Kaber DB (2017) Usability comparison of conventional direct control versus pattern recognition control of transradial prostheses. IEEE Trans Hum Mach Syst 99:1–12

    Google Scholar 

  6. Phinyomark A, Phukpattaranont P, Limsakul C (2012) Feature reduction and selection for EMG signal classification. Expert Syst Appl 39(8):7420–7431

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. Cireşan D C, Meier U, Masci J, Gambardella L M, Schmidhuber J (2011) High-performance neural networks for visual object classification. arXiv preprint arXiv:1102.0183

  9. Kim Y, Jernite Y, Sontag D, Rush AM (2016) Character-aware neural language models. AAAI 2016:2741–2749

    Google Scholar 

  10. Atzori M, Cognolato M, Müller H (2016) Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands. Front Neurorobot 10:9

    Article  Google Scholar 

  11. Park KH, Lee SW (2016) Movement intention decoding based on deep learning for multiuser myoelectric interfaces. In: Proc 4th international winter conf brain–computer interface (BCI), pp 1–2

  12. Geng W, Du Y, Jin W, Wei W, Hu Y, Li J (2016) Gesture recognition by instantaneous surface EMG images. Sci Rep 6:36571

    Article  Google Scholar 

  13. Niu XX, Suen CY (2012) A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recognit 45(4):1318–1325

    Article  Google Scholar 

  14. Bluche T, Ney H, Kermorvant C (2013) Tandem HMM with convolutional neural network for handwritten word recognition. In: IEEE international conference on acoustics, speech, and signal processing (ICASSP), pp 2390–2394

  15. Fang Y, Liu H (2014) Robust sEMG electrodes configuration for pattern recognition based prosthesis control. In: 2014 IEEE international conference on systems man and cybernetics (SMC), pp 2210–2215

  16. Atzori M, Gijsberts A, Castellini C, Caputo B, Hager AM, Elsig S, Giatsidis G, Bassetto F, Müller H (2014) Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Sci Data 1:140053

    Article  Google Scholar 

  17. Amma C, Krings T, Böer J, Schultz T (2015) Advancing muscle-computer interfaces with high-density electromyography. In: Proc ACM conference on human factors in computing systems (CHI’15). pp 929–938

  18. Phinyomark A, Phukpattaranont P, Limsakul C (2012) Feature reduction and selection for EMG signal classification. Expert Syst Appl 39(8):7420–7431

    Article  Google Scholar 

  19. Sharma A, Paliwal K (2008) A gradient linear discriminant analysis for small sample sized problem. Neural Process Lett 27(1):17–24

    Article  Google Scholar 

  20. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  21. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167

  22. Krizhevsky A, Sutskever I, Hinton G E (2012) Imagenet classification with deep convolutional neural networks. In: Proc neural information and processing systems, pp 1097–1105

  23. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado G S, Davis A, Dean J, Devin M, et al (2016) TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467

  24. Sutskever I, Martens J, Dahl GE, Hinton GE (2013) On the importance of initialization and momentum in deep learning. In: International conference on machine learning, pp 1139–1147

  25. Mahalanobis PC (1936) On the generalized distance in statistics. In: Proceedings of the national institute of science of India, vol 12, pp 49–55

  26. LeCun Y, Bottou L, Bengio Y, Haffner P et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  27. Blumer A, Ehrenfeucht A, Haussler D, Warmuth MK (1989) Learnability and the Vapnik–Chervonenkis dimension. J Ass Comput Mach 36(4):929–965

    Article  MathSciNet  Google Scholar 

  28. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167

    Article  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the support from the EU Seventh Framework Programme (FP7)-ICT under Grant no. 611391, Natural Science Foundation of China under Grant nos. 51575338, 51575407, 51475427, and the open fund of the key laboratory for metallurgical equipment and control of ministry of education in Wuhan University of Science and Technology under Grant no. 2017B03.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Hongfeng Chen & Yue Zhang

  2. Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Institute of Precision Manufacturing, 947 Heping Avenue, Wuhan, 430081, China

    Gongfa Li

  3. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China

    Yinfeng Fang

  4. Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, Winston Churchill Avenue, Portsmouth, Hampshire, PO1 2UP, UK

    Honghai Liu

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  1. Hongfeng Chen
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Correspondence to Yinfeng Fang.

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Chen, H., Zhang, Y., Li, G. et al. Surface electromyography feature extraction via convolutional neural network. Int. J. Mach. Learn. & Cyber. 11, 185–196 (2020). https://doi.org/10.1007/s13042-019-00966-x

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