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Related Experiment Videos

Advanced modeling environment for developing and testing FES control systems.

R Davoodi1, I E Brown, G E Loeb

  • 1A.E. Mann Institute for Biomedical Engineering, University of Southern California, 1042 West 36th Place DRB-B12, Los Angeles, CA 90089-1112, USA. davoodi@usc.edu

Medical Engineering & Physics
|December 18, 2002
PubMed
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This summary is machine-generated.

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We developed integrated tools to create realistic neuromusculoskeletal models for functional electrical stimulation (FES) controller design. These models offer a safe environment for FES research before clinical trials.

Area of Science:

  • Biomedical Engineering
  • Computational Neuroscience
  • Rehabilitation Engineering

Background:

  • Realistic neuromusculoskeletal models are crucial for designing and evaluating functional electrical stimulation (FES) controllers.
  • Current modeling approaches can be complex and time-consuming, hindering rapid development and testing.

Purpose of the Study:

  • To develop integrated musculoskeletal modeling tools that simplify the creation of realistic neuromusculoskeletal models.
  • To facilitate the design and evaluation of FES controllers in a safe, virtual environment prior to clinical application.

Main Methods:

  • Developed Musculoskeletal Modeling in Simulink (MMS) and Virtual Muscle software packages.
  • Integrated SIMM (commercial software) with custom tools for creating anatomically accurate musculoskeletal models.

Related Experiment Videos

  • Utilized Matlab toolboxes for sensorimotor control system development within the Simulink environment.

    • Main Results:

    • MMS converts SIMM-generated models into Simulink blocks, removing run-time constraints.
    • Virtual Muscle creates realistic Simulink muscle models responsive to natural recruitment or FES.
    • Enabled seamless integration of sensorimotor control models with musculoskeletal models in Simulink.

    Conclusions:

    • The developed integrated tools streamline the creation of complex neuromusculoskeletal models.
    • These tools provide a robust platform for safe and efficient FES controller design and evaluation.
    • Facilitates advanced research in FES applications and sensorimotor control systems.