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Related Experiment Videos

An optimal control model for analyzing human postural balance

A D Kuo1

  • 1Department of Mechanical Engineering and Applied Mechanics, University of Michigan, Ann Arbor 48109-2125.

IEEE Transactions on Bio-Medical Engineering
|January 1, 1995
PubMed
Summary
This summary is machine-generated.

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Optimal control and state estimation models human balance responses to perturbations. A sensorimotor control model explains human strategy selection by optimizing objectives like center of mass control and upright stance.

Area of Science:

  • Biomechanics
  • Human sensorimotor control
  • Robotics and control theory

Background:

  • Human upright balance relies on complex sensorimotor control.
  • Understanding the selection of specific control strategies in response to perturbations is crucial.
  • Existing models often simplify the interplay between control and biomechanical constraints.

Purpose of the Study:

  • To determine if optimal control and state estimation principles can explain human control strategy selection during balance.
  • To develop and validate a human sensorimotor control model capable of predicting balance responses.
  • To investigate the influence of various control objectives and system constraints on balance control.

Main Methods:

  • Assembled a human sensorimotor control model using linearized equations and full-state feedback with state estimation.

Related Experiment Videos

  • Employed gain-scheduling to address nonlinearities from biomechanical and control constraints.
  • Decoupled mechanics and transformed controls to analyze experimentally observed strategies and control objectives.

    • Main Results:

    • An objective function prioritizing center of mass excursion and upright stance deviation, utilizing system's fast modes, closely matched experimental data.
    • The model demonstrated that optimal control principles, considering inertial parameters and musculoskeletal geometry, can predict human balance control strategies.
    • Investigated the impact of time delays on control stability across different strategies.

    Conclusions:

    • Optimal control and state estimation provide a viable framework for explaining human sensorimotor control strategies in balance.
    • The developed model offers a predictive tool for human responses to balance perturbations, including uncertainty estimation.
    • Findings contribute to a deeper understanding of the neural control of posture and movement.