A two-joint human posture control model with realistic neural delays.

Area of Science:

• Biomechanics
• Robotics
• Control Theory
• Human Posture Regulation

Background:

• Human quiet standing involves complex sway patterns not easily modeled by simple engineering approaches.
• Accurate modeling of posture regulation is crucial for understanding human movement and developing assistive technologies.

Purpose of the Study:

• To develop a nonlinear optimal control model for human posture regulation during quiet standing.
• To replicate experimentally observed sway patterns and coordinated nonlinear responses using a computational model.

Main Methods:

• Developed a model incorporating double inverted pendulum dynamics in the sagittal plane, controlled by ankle and hip torques.
• Defined a nonlinear performance measure based on center of pressure and control torques.
• Incorporated realistic sensory and motor delays into the dynamic model.
• Utilized model predictive control (MPC) to solve the nonlinear quartic regulator problem.

Main Results:

• The developed nonlinear optimal control model successfully replicated experimentally observed human sway patterns during quiet standing.
• The model demonstrated coordinated nonlinear responses consistent with human postural control.
• The model is predicted to utilize less muscular energy compared to other control strategies.

Conclusions:

• The nonlinear optimal control model provides a more accurate representation of human posture regulation than previous engineering models.
• The model's ability to replicate sway and coordinate responses highlights its potential for understanding human biomechanics.
• The methodology is adaptable for more complex biomechanical models and applications in robotics and rehabilitation.