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SE-TCN network for continuous estimation of upper limb joint angles.

Xiaoguang Liu1,2, Jiawei Wang1,2, Tie Liang1,2

  • 1College of Electronic and Information Engineering, Hebei University, Baoding, Hebei, China.

Mathematical Biosciences and Engineering : MBE
|March 11, 2023
PubMed
Summary

This study introduces a novel SE-TCN model for predicting upper limb joint angles using surface electromyographic signals (sEMG). The advanced model significantly improves accuracy in controlling exoskeleton robots and intelligent prostheses.

Keywords:
SE-TCN networkcontinuous angle estimationhuman-computer interactionsEMG

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Machine Learning

Background:

  • Surface electromyographic signals (sEMG) enable control of exoskeleton robots and prostheses.
  • Existing sEMG-controlled upper limb robots suffer from inflexible joint control.
  • Accurate prediction of joint angles from sEMG is crucial for advanced rehabilitation robotics.

Purpose of the Study:

  • To develop an improved method for predicting upper limb joint angles from sEMG signals.
  • To enhance the accuracy and flexibility of sEMG-controlled upper limb rehabilitation robots.
  • To introduce a novel SE-TCN model integrating Temporal Convolutional Networks (TCN) with Squeeze-and-Excitation Networks (SE-Net).

Main Methods:

  • Utilized a Temporal Convolutional Network (TCN) architecture with expanded depth for feature extraction.
  • Integrated Squeeze-and-Excitation Networks (SE-Net) to enhance the TCN's ability to capture temporal sequence characteristics of muscle activity.
  • Collected sEMG data from ten subjects performing seven upper limb movements, recording elbow angle (EA), shoulder vertical angle (SVA), and shoulder horizontal angle (SHA).

Main Results:

  • The proposed SE-TCN model demonstrated superior performance compared to traditional Backpropagation (BP) and Long Short-Term Memory (LSTM) networks.
  • SE-TCN achieved significant reductions in Root Mean Square Error (RMSE): 25.0% for EA, 38.6% for SHA, and 45.6% for SVA compared to BP.
  • SE-TCN showed improvements over LSTM with RMSE reductions of 36.8% for EA, 43.6% for SHA, and 49.5% for SVA.
  • R-squared values for SE-TCN surpassed BP and LSTM by notable margins across all measured joint angles.

Conclusions:

  • The SE-TCN model offers a highly accurate and effective method for estimating upper limb joint angles from sEMG signals.
  • This approach holds significant potential for advancing the control and flexibility of future upper limb rehabilitation robots.
  • The findings suggest that the SE-TCN model can overcome limitations of existing sEMG-based control systems.