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Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from

Ho Chit Siu1, Ana M Arenas2, Tingxiao Sun3

  • 1Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA. hoseasiu@mit.edu.

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Summary
This summary is machine-generated.

This study introduces an adaptive surface electromyography (sEMG) controller for exoskeletons, enabling anticipatory control. The system personalizes muscle signal recognition, improving exoskeleton responsiveness and adaptability for users.

Keywords:
exoskeletonshuman experimentslearning from demonstrationsurface electromyography

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Neuroscience

Background:

  • Upper-extremity exoskeletons offer potential for augmentation, assistance, and rehabilitation.
  • Current exoskeleton control methods (switches, force sensors, sEMG) are often reactive or require manual tuning.
  • Surface electromyography (sEMG) offers anticipatory control but typically needs precise sensor placement.

Purpose of the Study:

  • To develop an adaptive sEMG-based controller for exoskeletons that enables anticipatory control.
  • To create a personalized sEMG feature classifier that learns user muscle activation patterns.
  • To overcome limitations of traditional sEMG control, including sensor placement sensitivity and adaptability.

Main Methods:

  • Implemented an adaptive learning-from-demonstration control system for an upper-extremity exoskeleton.
  • Developed a personalized sEMG feature classifier to map muscle activation to desired system states.
  • Validated the system with 18 subjects performing a book-placement task using a thumb exoskeleton.

Main Results:

  • The adaptive controller enabled anticipatory control of the exoskeleton.
  • The system demonstrated robustness to novice sEMG sensor placement and muscle shifts.
  • Short training times were achieved, with potential for improved intent recognition and adaptation to user fatigue.

Conclusions:

  • Adaptive sEMG control offers a promising approach for intuitive and responsive exoskeleton operation.
  • This learning-based system enhances user experience by adapting to individual physiology and reducing reliance on expert calibration.
  • The method holds potential for advancing assistive and rehabilitative robotics through personalized, anticipatory control.