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Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography.

Yonglin Han1, Qing Tao1, Xiaodong Zhang2

  • 1School of Mechanical Engineering, Xinjiang University, Urumqi 830047, China.

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|February 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel model for estimating lower limb joint angles using surface electromyography (sEMG) signals, significantly improving accuracy in rehabilitation and robotics. The CB-TCN model enhances feature extraction for better motion analysis.

Keywords:
CBAMTCNdifferent movement patternsmultijoint angle estimationsEMG

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Area of Science:

  • Biomechanics and Robotics
  • Biomedical Engineering
  • Machine Learning for Healthcare

Background:

  • Accurate estimation of multijoint angles is crucial for lower limb rehabilitation, motion control, and exoskeleton robotics.
  • Current methods using surface electromyography (sEMG) face challenges in diverse movement patterns like walking and squatting.
  • Developing robust models for continuous joint angle prediction from sEMG is an ongoing research need.

Purpose of the Study:

  • To propose and validate a novel model, the CB-TCN (temporal convolutional network + convolutional block attention module + temporal convolutional network), for continuous multijoint angle estimation in the lower limb.
  • To enhance feature extraction and prediction accuracy by integrating temporal convolutional networks (TCNs) with convolutional block attention modules (CBAMs).
  • To improve the model's generalization across different movement patterns through data augmentation techniques.

Main Methods:

  • Development of the CB-TCN model, combining TCNs for temporal feature extraction and CBAMs for attention-based feature enhancement.
  • Implementation of a sliding window data augmentation method to increase training sample size and model adaptability.
  • Experimental validation involving 8 subjects performing four distinct lower limb movements: walking, obstacle crossing, squatting, and knee flexion-extension.

Main Results:

  • The CB-TCN model demonstrated superior accuracy and robustness compared to traditional models in predicting lower limb joint angles.
  • Achieved high performance metrics, including R² values up to 0.9718, Root Mean Square Error (RMSE) as low as 1.2648°, and Normalized RMSE (NRMSE) as low as 0.05234 for knee angle prediction during walking.
  • The model effectively captured temporal dynamics and focused on salient features for improved prediction.

Conclusions:

  • The proposed CB-TCN model integrating TCN and CBAM shows significant advantages for predicting lower limb joint angles from sEMG signals.
  • The approach offers a promising solution for advancing lower limb rehabilitation, motion analysis, and the development of intelligent robotic systems.
  • The model's enhanced feature extraction and generalization capabilities address key challenges in sEMG-based motion estimation.