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Towards Active Muscle Pattern Analysis for Dynamic Hand Motions via sEMG

  • Jiahan Li
  • Yinfeng Fang
  • Yongan Huang
  • Gongfa Li
  • Zhaojie Ju
  • Honghai Liu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 840)

Abstract

Surface Electromyographys (sEMG) as a widespread human-computer interaction method can reflect the activity of human muscles. When the human forearm finishes different hand motions, there will be strong sEMG signals in different regions of the skin surface. This paper investigates the mapping relationship between sEMG signal patterns and the dynamic hand motions. Four different hand motions are studied based on the extracted signal with mean absolute value (MAV) features and the shape-preserving piecewise cubic interpolation method. In the experiments, a 16-channel electrode sleeve is used to collect 9-subject EMG signals. According to the distribution of electrodes in the forearm, the forearm surface is divided into 8 different muscle regions. The preliminary experimental results show that different hand motions can cause different distribution of sEMG signals in different regions. It confirms that different subjects show similar patterns for the same motions. The experimental results can be applied as new sEMG features with a higher computational speed.

Keywords

sEMG MAV Shape-preserving piecewise cubic interpolation Local maximum Muscle regions 

Notes

Acknowledgment

The authors would like to acknowledge the support from the Natural Science Foundation of China under Grant No. 51575412, 51575338 and 51575407, the EU Seventh Framework Programme (FP7)-ICT under Grant No. 611391, the Grants of National Defense Pre-Research Foundation of Wuhan University of Science and Technology (GF201705) and the Research Project of State Key Lab of Digital Manufacturing Equipment & Technology of China under Grant No. DMETKF2017003. And this paper is funded by Wuhan University of Science and Technology graduate students’ short-term study abroad special funds.

References

  1. 1.
    Yinfeng, F., Honghai, L., Gongfa, L., Xiangyang, Z.: A multichannel surface EMG system for hand motion recognition. Int. J. Humanoid Robot. 12(2), 1550011 (2015)CrossRefGoogle Scholar
  2. 2.
    Yaxu, X., Zhaojie, J., Jing, C., Honghai, L.: Multiple sensors based hand motion recognition using adaptive directed acyclic graph. Appl. Sci. 7(4), 358 (2017)CrossRefGoogle Scholar
  3. 3.
    Rui, S., Rong, S., Kai-yu, T.: Complexity analysis of EMG signals for patients after stroke during robot-aided rehabilitation training using fuzzy approximate entropy. IEEE Trans. Neural Syst. Rehabil. Eng. 22(5), 1013–1019 (2013)Google Scholar
  4. 4.
    Khezri, M., Jahed, M., Sadati, N.: Neuro-fuzzy surface EMG pattern recognition for multifunctional hand prosthesis control. In: 2007 IEEE International Symposium on Industrial Electronics, Spain, pp. 269–274 (2007)Google Scholar
  5. 5.
    Rezwanul, M., Ahsan, M., Ibrahimy, I., Othman, K.: Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN). In: 4th International Conference on Mechatronics, Malaysia, vol. 4, pp. 1–6 (2015)Google Scholar
  6. 6.
    Wan-Ting, S., Zong-Jhe, L., Shih-Tsang, T., Tsorng-Lin, C., Chia-Yen, Y.: A bionic hand controlled by hand gesture recognition based on surface EMG signals: a preliminary study. Biocybern. Biomed. Eng. 38(1), 126–135 (2018)CrossRefGoogle Scholar
  7. 7.
    Manea, S., Kamblib, R., Kazic, F., Singhc, N.: Hand motion recognition from single channel surface EMG using wavelet & artificial neural network. Procedia Comput. Sci. 49(1), 58–65 (2015)CrossRefGoogle Scholar
  8. 8.
    An-Chih, T., Jer-Junn, L., Ta-Te, L.: A novel STFT-ranking feature of multi-channel EMG for motion pattern recognition. Expert Syst. Appl. 42(7), 3327–3341 (2014)Google Scholar
  9. 9.
    Qichuan, D., Jianda, H., Xingang, Z., Yang, C.: Missing-data classification with the extended full-dimensional gaussian mixture model: applications to EMG-based motion recognition. IEEE Trans. Ind. Electron. 62(8), 4994–5005 (2015)CrossRefGoogle Scholar
  10. 10.
    Boyang, Z., Erwei, Y., Jun, J., Zongtan, Z.: A synchronous robot control system based on the sEMG signals of human upper limb motions. In: Proceedings of the 36th Chinese Control Conference, China, pp. 5136–5140 (2017)Google Scholar
  11. 11.
    Francesca, P., Matteo, C., Arjan, G., Henning, M.: Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data. In: 2017 International Conference on Rehabilitation Robotics (ICORR), UK, pp. 17–20 (2017)Google Scholar
  12. 12.
    Xiangxin, L., Oluwarotimi, W., Samuel, X., Hui, Z.: A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees. J. NeuroEng. Rehabil. 14(1), 2–5 (2017)CrossRefGoogle Scholar
  13. 13.
    Ying, S., Cuiqiao, L., Gongfa, L., Guozhang, J., Du, J., Honghai, L., Zhigao, Z.: Gesture recognition based on kinect and sEMG signal fusion. Mobile Netw. Appl. 1–9 (2018)Google Scholar
  14. 14.
    Moore, S., McGuigan, M.: Functional wavelet resolution of the sEMG frequency spectrum to represent high and low frequency motor unit recruitment in human lower limb muscles. Int. J. Sci. Med. Sport 20, 73–75 (2017)CrossRefGoogle Scholar
  15. 15.
    Jose, E., Cavazos, J., Halford, M.: Use of sEMG to inform GTC seizure semiology. Neurology 88(2), 2–11 (2017)Google Scholar
  16. 16.
    Merijn, E., Maarten, A., Ludi, S., Dieta, B., Alfons, B., Ferdinand, H.: Predicting 3D lip movement using facial sEMG: a first step towards estimating functional and aesthetic outcome of oral cancer surgery. Med Biol Eng Comput 55(4), 1–11 (2016)Google Scholar
  17. 17.
    Ganesh, N., Easter, S., Sridhar, P., Arjunan, A., Acharyya, D., Kumar, A.: Predicting 3D lip movement using facial sEMG: an ICA-EBM-based sEMG classifier for recognizing lower limb movements in individuals with and without knee pathology. IEEE Trans. Neural Syst. Rehabil. Eng. 26(3), 675 (2018)CrossRefGoogle Scholar
  18. 18.
    Shogo, O., Misaki, S., Hiroki, T., Takahiro, N.: Development of diagnosis evaluation system of facial nerve paralysis using sEMG. In: The 2017 International Conference on Artificial Life and Robotics, Japan, pp. 11893–11908 (2017)Google Scholar
  19. 19.
    Yinfeng, F., Nalinda, H., Dalin, Z., Honghai, L.: Multi-modal sensing techniques for interfacing hand prostheses: a review. IEEE Sens. J. 15(11), 6065–6076 (2015)CrossRefGoogle Scholar
  20. 20.
    Fritsch, F., Carlson, R.: Monotone piecewise cubic interpalation. SIAM J. Numer. Anal. 17(2), 238–246 (1980)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jiahan Li
    • 1
  • Yinfeng Fang
    • 2
  • Yongan Huang
    • 5
  • Gongfa Li
    • 1
    • 3
    • 4
  • Zhaojie Ju
    • 2
  • Honghai Liu
    • 2
  1. 1.Key Laboratory of Metallurgical Equipment and Control TechnologyWuhan University of Science and Technology, Ministry of EducationWuhanChina
  2. 2.School of ComputingUniversity of PortsmouthPortsmouthUK
  3. 3.Research Center for Biomimetic Robot and Intelligent Measurement and ControlWuhan University of Science and TechnologyWuhanChina
  4. 4.Institute of Precision Manufacturing, Wuhan University of Science and TechnologyWuhanChina
  5. 5.State Key Lab of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and Technology WuhanWuhanChina

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