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Adaptive robot mediated upper limb training using electromyogram-based muscle fatigue indicators.

Azeemsha Thacham Poyil1, Volker Steuber1, Farshid Amirabdollahian1

  • 1School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom.

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|May 30, 2020
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Summary
This summary is machine-generated.

This study used electromyogram (EMG) features to adapt robot-assisted upper limb training difficulty based on muscle fatigue. This approach enabled prolonged training sessions with more repetitions compared to manual or no adaptation.

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Area of Science:

  • Rehabilitation Engineering
  • Human-Robot Interaction
  • Sports Science

Background:

  • Upper limb rehabilitation training often overlooks muscle fatigue.
  • Electromyogram (EMG) features can serve as indicators of muscle fatigue.

Purpose of the Study:

  • To investigate the use of EMG features for auto-adapting task difficulty in human-robot interaction for upper limb training.
  • To prolong training interaction time and increase repetitions by adapting to muscle fatigue.

Main Methods:

  • Collected EMG data from three upper limb gross-muscles of 30 healthy participants.
  • Implemented a progressive strength training protocol with robotic adaptation based on fatigue indicators.
  • Compared a fatigue-based robotic adaptation group with manual and no adaptation control groups.

Main Results:

  • Participants in the fatigue-based robotic adaptation group performed prolonged training with more repetitions.
  • Robotic adaptation based on muscle fatigue significantly increased training interaction time compared to control groups.
  • Perception of task difficulty changes was evaluated across the intervention and control groups.

Conclusions:

  • Fatigue-based robotic adaptation is effective for prolonging progressive strength training and increasing repetitions.
  • This approach holds potential for adapting robot-assisted training for stroke patients by monitoring their muscular state.
  • Future applications may involve optimizing rehabilitation by adjusting difficulty based on real-time fatigue indicators.