Gesture recognition from surface electromyography signals based on the SE-DenseNet network.

Journal: Biomedizinische Technik. Biomedical engineering
Published Date: Jan 29, 2025

Abstract

OBJECTIVES: In recent years, significant progress has been made in the research of gesture recognition using surface electromyography (sEMG) signals based on machine learning and deep learning techniques. The main motivation for sEMG gesture recognition research is to provide more natural, convenient, and personalized human-computer interaction, which makes research in this field have considerable application prospects in rehabilitation technology. However, the existing gesture recognition algorithms still need to be further improved in terms of global feature capture, model computational complexity, and generalizability.

Authors

  • Ying Xiang
    College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, Shaanxi, China.
  • Wei Zheng
    School of Computer Engineering, Jinling Institute of Technology, Nanjing, 211169, China. zhengwei@jit.edu.cn.
  • Jiajia Tang
  • You Dong
    College of Ocean, Jiangsu University of Science and Technology, Zhenjiang, China.
  • Yuhao Pang
    College of Ocean, Jiangsu University of Science and Technology, Zhenjiang, China.

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