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Feedback parameters for a closed-loop multiple-input multiple-output model of the upper limb.

Ian Syndergaard1, Daniel B Free1, Dario Farina2

  • 1Mechanical Engineering, Brigham Young University, Provo, Utah, United States of America.

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|June 30, 2025
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Summary
This summary is machine-generated.

This study presents a method and parameter estimates for creating closed-loop, multi-input multi-output (MIMO) neuromusculoskeletal models of the upper limb. These models are crucial for understanding motor control and simulating limb movement.

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Area of Science:

  • Neuroscience
  • Biomechanics
  • Systems Biology

Background:

  • Closed-loop and multi-input multi-output (MIMO) models are essential for simulating upper limb motor control.
  • Existing models lack comprehensive, large-scale linear neuromusculoskeletal representations that are both closed-loop and MIMO.
  • A significant barrier to developing such models is the absence of established feedback parameter values (gains and delays).

Purpose of the Study:

  • To introduce a methodology for constructing MIMO models of short-loop afferent feedback in the upper limb.
  • To provide estimated average values and ranges for feedback parameters based on existing literature.

Main Methods:

  • Integrated feedback parameter data from 26 previous studies.
  • Applied principles of system stability and behavior to refine parameter estimates.
  • Developed a linear model incorporating 13 superficial muscles and 7 joint degrees of freedom (shoulder to wrist).
  • Included homonymous feedback (Golgi tendon organs) and homonymous/heteronymous feedback (muscle spindles).

Main Results:

  • Estimated feedback gains and delays for the upper limb neuromusculoskeletal model.
  • Validated muscle spindle feedback gains by comparing signs with known central delay differences.
  • Validated delay times by comparing estimated delays with measured innervation lengths, showing strong fit for efferent (R=0.88) and moderate fit for afferent (R=0.65) delays.
  • Demonstrated the impact of feedback on model behavior and compared it to experimental observations.

Conclusions:

  • Successfully developed a method for creating MIMO neuromusculoskeletal models with estimated feedback parameters.
  • The model provides a foundation for simulating upper limb motor control with realistic feedback mechanisms.
  • Findings contribute to a better understanding of how neural feedback influences upper limb biomechanics and movement.