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A Pilot Study on Motion Intention Mapping and Direct Myoelectric Control Method for Prosthetic Knee Based on LSTM

Xiaoming Wang1, Yuanhua Li1, Xiaoying Xu2

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

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Summary

This study introduces a novel direct myoelectric control for prosthetic knees using an LSTM network. It improves adaptability and human-machine coordination for smoother, more natural prosthetic limb movement.

Keywords:
LSTM neural networkdirect myoelectric controlhuman-machine coupling modelprosthetic kneesurface electromyography (sEMG)

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Neuroscience

Background:

  • Prosthetic knees require advanced control for adaptability and natural movement.
  • Current myoelectric control methods face challenges in real-time coordination and responsiveness.

Purpose of the Study:

  • To develop a direct myoelectric control method for intelligent prosthetic knees.
  • To enhance human-machine coordination and adaptability using an LSTM network and coupling model.

Main Methods:

  • Collected multichannel surface electromyography (sEMG) and knee joint angle data during walking.
  • Extracted time-domain features to build an LSTM prediction model for muscle activity-joint kinematics mapping.
  • Integrated a human-machine coupling dynamics model with a hydraulic actuation system for a prosthetic knee control framework.

Main Results:

  • The LSTM model demonstrated superior prediction accuracy and temporal consistency compared to traditional neural networks.
  • Achieved continuous damping adjustment and smooth gait transitions in a variable-damping prosthetic knee.
  • Validated the effectiveness of the direct myoelectric control for human-machine coordinated prosthetic limb function.

Conclusions:

  • The proposed LSTM-based direct myoelectric control method significantly enhances prosthetic knee adaptability and human-machine coordination.
  • This approach offers a feasible and effective solution for intelligent prosthetic limb control, improving user mobility.