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sEMG-Based Gesture Recognition with Convolution Neural Networks

Author

Listed:
  • Zhen Ding

    (School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150000, China)

  • Chifu Yang

    (School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150000, China)

  • Zhihong Tian

    (Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510000, China)

  • Chunzhi Yi

    (School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150000, China)

  • Yunsheng Fu

    (Institute of Computer Application, China Academy of Engineer Physics, Mianyang 621000, China)

  • Feng Jiang

    (School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150000, China)

Abstract

The traditional classification methods for limb motion recognition based on sEMG have been deeply researched and shown promising results. However, information loss during feature extraction reduces the recognition accuracy. To obtain higher accuracy, the deep learning method was introduced. In this paper, we propose a parallel multiple-scale convolution architecture. Compared with the state-of-art methods, the proposed architecture fully considers the characteristics of the sEMG signal. Larger sizes of kernel filter than commonly used in other CNN-based hand recognition methods are adopted. Meanwhile, the characteristics of the sEMG signal, that is, muscle independence, is considered when designing the architecture. All the classification methods were evaluated on the NinaPro database. The results show that the proposed architecture has the highest recognition accuracy. Furthermore, the results indicate that parallel multiple-scale convolution architecture with larger size of kernel filter and considering muscle independence can significantly increase the classification accuracy.

Suggested Citation

  • Zhen Ding & Chifu Yang & Zhihong Tian & Chunzhi Yi & Yunsheng Fu & Feng Jiang, 2018. "sEMG-Based Gesture Recognition with Convolution Neural Networks," Sustainability, MDPI, vol. 10(6), pages 1-12, June.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:1865-:d:150509
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    References listed on IDEAS

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    1. Yongli Zhang & Sanggyun Na & Jianguang Niu & Beichen Jiang, 2018. "The Influencing Factors, Regional Difference and Temporal Variation of Industrial Technology Innovation: Evidence with the FOA-GRNN Model," Sustainability, MDPI, vol. 10(1), pages 1-19, January.
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