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Related Experiment Videos

Fatigue compensation during FES using surface EMG.

Jeffrey Winslow1, Patrick L Jacobs, Dejan Tepavac

  • 1The Miami Project To Cure Paralysis, University of Miami School of Medicine, Miami, FL 33101, USA. jwinslow@cableone.net

Journal of Electromyography and Kinesiology : Official Journal of the International Society of Electrophysiological Kinesiology
|October 24, 2003
PubMed
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This study introduces an artificial neural network (ANN) system to combat muscle fatigue during functional electrical stimulation (FES). The system uses surface electromyography (EMG) to automatically adjust stimulation, improving FES effectiveness for spinal cord injury (SCI) patients.

Area of Science:

  • Biomedical Engineering
  • Neurorehabilitation
  • Robotics

Background:

  • Muscle fatigue significantly hinders functional electrical stimulation (FES) effectiveness in spinal cord injury (SCI) rehabilitation.
  • Manual adjustments of stimulation intensity are required to counteract muscle fatigue and maintain functional movement, posing a challenge for FES systems.

Purpose of the Study:

  • To develop and evaluate an artificial neural network (ANN) system for automatic fatigue compensation in FES.
  • To maintain a constant joint angle during FES by predicting and compensating for muscle fatigue using surface electromyography (EMG) signals.

Main Methods:

  • Developed an ANN system utilizing processed surface EMG signals as input to generate fatigue compensation control signals.
  • Conducted experiments on able-bodied subjects to induce muscle fatigue in biceps and quadriceps via continuous electrical stimulation.

Related Experiment Videos

  • Recorded surface EMG and joint angles, manually adjusting stimulation intensity to create training data for the ANN.

    • Main Results:

    • The ANN system successfully predicted, on average, 78.5% of the instances requiring stimulation increases to maintain constant joint angles.
    • Demonstrated the feasibility of using surface EMG feedback for intelligent fatigue compensation within an FES control system.

    Conclusions:

    • The developed ANN system shows promise in automatically managing muscle fatigue during FES.
    • This approach could enhance the efficacy of FES for restoring functional movements in individuals with SCI.