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Dynamic Simulation of Human Gait Model With Predictive Capability.

Jinming Sun1, Shaoli Wu2, Philip A Voglewede2

  • 1General Motors Company, Milford, MI 48380 e-mail: .

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|December 15, 2017
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Summary

The central nervous system (CNS) may use predictive control for human gait, not just feedback. A new dynamic model simulates gait, showing CNS predictive principles are key for movement control.

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Area of Science:

  • Biomechanics
  • Robotics
  • Neuroscience

Background:

  • Human gait control is traditionally explained by classical feedback mechanisms reacting to past errors.
  • The central nervous system's (CNS) precise control suggests more sophisticated underlying principles may be involved.

Purpose of the Study:

  • To propose and validate a novel dynamic model of human gait incorporating predictive control principles.
  • To investigate the role of predictive control in CNS gait regulation.

Main Methods:

  • Development of a seven-segment, nine-degree-of-freedom (DOF) dynamic plant model for human gait.
  • Implementation of a model predictive control (MPC) strategy to represent the CNS controller.
  • Validation of the plant model using experimental data from able-bodied human subjects.

Main Results:

  • The developed dynamic model successfully simulated able-bodied human gait kinematics.
  • Simulation results demonstrated close agreement between the model's output and experimental data.
  • The model's predictive control approach showed potential for explaining CNS gait regulation.

Conclusions:

  • The central nervous system (CNS) likely employs a predictive control strategy, combined with feedback, for human gait.
  • The developed dynamic model provides a framework for understanding CNS predictive control in locomotion.
  • This predictive approach offers a more comprehensive explanation of human gait dynamics than classical feedback alone.