Dynamic Simulation of Human Gait Model With Predictive Capability

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.