Advertisement

Classification Methods of sEMG Through Weighted Representation-Based K-Nearest Neighbor

  • Shuai Pan
  • Jing JieEmail author
  • Kairui Liu
  • Jinrong Li
  • Hui Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11743)

Abstract

As a valuable bio-electrical signal, surface electromyography (sEMG) can be adopted to predict the user’s motion gestures in human-machine interaction, but its validity severely depends on the accuracy of patterns recognition. In order to improve the recognition accuracy, the paper introduced a weighted representation-based k-nearest neighbor (WRKNN) to classify different hand motion patterns based on the forearm sEMG signals. All the signals were collected from 8 able-bodied volunteers through a sEMG acquisition system with 16 channels, and the root mean square (RMS) feature with the window size of 300 ms and the window shift of 100 ms were used to acquire the feature data. Based on the average classification accuracy and the standard deviation, the proposed algorithm and its improved version (weighted local mean representation-based k-nearest neighbor, WLMRKNN) were compared with k-nearest neighbor (KNN) and BP neural network. The experimental results show that WRKNN and WLMRKNN are superior to KNN and BP network with the best classification accuracy, and can be widely applied in the pattern recognition of sEMG in future.

Keywords

sEMG Pattern recognition Classification K-nearest neighbor 

References

  1. 1.
    Fang, Y., Hettiarachchi, N., Zhou, D., et al.: Multi-modal sensing techniques for interfacing hand prostheses: a review. IEEE Sens. J. 15(11), 6065–6076 (2016)CrossRefGoogle Scholar
  2. 2.
    Ding, Q.-C., Xiong, A.-B., Zhao, X.-G., Han, J.-D.: A review on researches and applications of sEMG-based motion intent recognition methods. Acta Automatica Sinica 42(1), 13–25 (2016). (in Chinese)Google Scholar
  3. 3.
    Wu, C.-C., Xiong, P.-W., Zeng, H., Xu, B.-G., Song, A.-G.: A control strategy for prosthetic hand based on EEG and sEMG. Acta Automatica Sinica 44(4), 676–684 (2018). (in Chinese)Google Scholar
  4. 4.
    Fang, Y., Liu, H.: Robust sEMG electrodes configuration for pattern recognition based prosthesis control. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, CA, USA, pp. 2210–2215. IEEE (2014)Google Scholar
  5. 5.
    Fang, Y., Zhu, X., Liu, H.: Development of a surface EMG acquisition system with novel electrodes configuration and signal representation. In: Lee, J., Lee, M.C., Liu, H., Ryu, J.-H. (eds.) ICIRA 2013. LNCS (LNAI), vol. 8102, pp. 405–414. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40852-6_41CrossRefGoogle Scholar
  6. 6.
    Richman, J.S., Randall, M.J.: Physiological time-series analysis, using approximate entropy and sample entropy. Am. J. Physiol. Heart Circulatory Physiol. 278(6), H2039–H2049 (2000)CrossRefGoogle Scholar
  7. 7.
    Phinyomark, A., Phukpattaranont, P., Limsakul, C.: Feature reduction and selection for EMG signal classification. Expert Syst. Appl. 39(8), 7420–7431 (2012)CrossRefGoogle Scholar
  8. 8.
    Phinyomark, A., Quaine, F., Charbonnier, S., Serviere, C., Tarpin-Bernard, F., Laurillau, Y.: EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Syst. Appl. 40(12), 4832–4840 (2013)CrossRefGoogle Scholar
  9. 9.
    Zardoshtikermani, M., Wheeler, B.C., Badie, K., et al.: EMG feature evaluation for movement control of upper extremity prostheses. IEEE Trans. Rehabil. Eng. 3(4), 324–333 (1995)CrossRefGoogle Scholar
  10. 10.
    Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefGoogle Scholar
  11. 11.
    Kim, K.S., Choi, H.H., Moon, C.S., et al.: Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions. Curr. Appl. Phys. 11(3), 740–745 (2011)CrossRefGoogle Scholar
  12. 12.
    Zhao, J., Xie, Z., Jiang, L., et al.: EMG control for a five-fingered underactuated prosthetic hand based on wavelet transform and sample entropy. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China, pp. 3215–3220. IEEE (2006)Google Scholar
  13. 13.
    Gou, J., et al.: Locality constrained representation-based K-nearest neighbor classification. Knowl.-Based Syst. 167, 38–52 (2019)CrossRefGoogle Scholar
  14. 14.
    Mitani, Y., Hamamoto, Y.: A local mean-based nonparametric classifier. Pattern Recogn. Lett. 27(10), 1151–1159 (2006)CrossRefGoogle Scholar
  15. 15.
    Gou, J., et al.: A multi-local means based nearest neighbor classifier. In: IEEE International Conference on Tools with Artificial Intelligence, Boston, MA, USA, pp. 448–452. IEEE Computer Society (2017)Google Scholar
  16. 16.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shuai Pan
    • 1
  • Jing Jie
    • 1
    Email author
  • Kairui Liu
    • 1
  • Jinrong Li
    • 1
  • Hui Zheng
    • 1
  1. 1.Zhejiang University of Science and TechnologyHangzhouChina

Personalised recommendations