Surface EMG hand gesture recognition system based on PCA and GRNN

  • Jinxian Qi
  • Guozhang Jiang
  • Gongfa LiEmail author
  • Ying Sun
  • Bo Tao
Original Article


The principal component analysis method and GRNN neural network are used to construct the gesture recognition system, so as to reduce the redundant information of EMG signals, reduce the signal dimension, improve the recognition efficiency and accuracy, and enhance the feasibility of real-time recognition. Using the means of extracting key information of human motion, the specific action mode is identified. In this paper, nine static gestures are taken as samples, and the surface EMG signal of the arm is collected by the electromyography instrument to extract four kinds of characteristics of the signal. After dimension reduction and neural network learning, the overall recognition rate of the system reached 95.1%, and the average recognition time was 0.19 s.


sEMG Gesture recognition Feature reduction PCA GRNN Machine learning 



This work was supported by Grants of National Natural Science Foundation of China (Grant Nos. 51575407, 51505349, 51575338, 51575412 and 61733011) and the Grants of National Defense Pre-research Foundation of Wuhan University of Science and Technology (GF201705).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of EducationWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Key Laboratory of Mechanical Transmission and Manufacturing EngineeringWuhan University of Science and TechnologyWuhanChina
  3. 3.Research Center of Biologic Manipulator and Intelligent Measurement and Control, Wuhan University of Science and TechnologyWuhanChina
  4. 4.Institute of Precision ManufacturingWuhan University of Science and TechnologyWuhanChina

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