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Processing Surface EMG Signals for Exoskeleton Motion Control.

Gui Yin1,2, Xiaodong Zhang1,2, Dawei Chen3

  • 1Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

Frontiers in Neurorobotics
|August 9, 2020
PubMed
Summary
This summary is machine-generated.

This study developed an intuitive EMG-driven speed-control for robotic exoskeletons and treadmills, improving human-machine coordination for assistive ambulation and potential gait rehabilitation.

Keywords:
electromyographyexoskeletongaitrehabilitationtreadmillvolitional control

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Neuroscience

Background:

  • Surface electromyography (sEMG) signals are used for controlling robotic assistive devices.
  • Challenges remain in improving system accuracy and signal processing for noise reduction.

Purpose of the Study:

  • To present procedures and validation for EMG-driven speed-control of an exoskeleton and integrated treadmill.
  • To enhance user-system interaction for robotic assistive ambulation.

Main Methods:

  • Extracted gait cycle duration (GCD) from sEMG using autocorrelation and Bayesian fusion algorithms.
  • Programmed GCDs to control exoskeleton motion speed.
  • Validated the system with 6 healthy volunteers.

Main Results:

  • Autocorrelation algorithm extracted GCD from individual muscle contractions with variability.
  • Bayesian fusion processed multiple muscle GCDs to yield a final GCD with minimal variance.
  • Fused GCD effectively controlled exoskeleton and treadmill speeds, showing higher EMG amplitude and shorter GCD at faster walking speeds.
  • Exoskeleton joint motion tracks matched algorithm-generated trajectories with high agreement.

Conclusions:

  • EMG-driven speed-control offers intuitive human-machine inter-limb coordination for robotic assistive systems.
  • The system shows potential for gait rehabilitation in patients with incomplete spinal cord injuries.