Two-dimensional identification of lower limb gait features based on the variational modal decomposition of sEMG signal and convolutional neural network.

Journal: Gait & posture
PMID: 39754859

Abstract

BACKGROUND: Gait feature recognition is crucial to improve the efficiency and coordination of exoskeleton assistance. The recognition methods based on surface electromyographic (sEMG) signals are popular. However, the recognition accuracy of these methods is poor due to ignoring the correlation of the time series of sEMG signals. Therefore, this paper proposes a two-dimensional recognition method of lower limb gait features based on sEMG signal decomposition under multiple motion modes to improve the accuracy and robustness of gait recognition.

Authors

  • Qiming Liu
    Department of Cardiovascular Medicine/Cardiac Catheterization Lab, Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan Province, People's Republic of China. qimingliu@csu.edu.cn.
  • Shan Wang
    Department of Echocardiography & Noninvasive Cardiology Laboratory, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, Chengdu, Sichuan, 610047, China.
  • Yuxing Dai
    Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Tianjin 300401, PR China; Hebei Key Laboratory of Robot Sensing and Human-robot Interaction, Hebei University of Technology, Tianjin 300401, PR China; School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, PR China.
  • Xingfu Wu
    State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082 PR China.
  • Shijie Guo
  • Weihua Su
    Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Tianjin 300401, PR China; Hebei Key Laboratory of Robot Sensing and Human-robot Interaction, Hebei University of Technology, Tianjin 300401, PR China; School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, PR China. Electronic address: weihua@hebut.edu.cn.

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