Gait simulation via a 6-DOF parallel robot with iterative learning control

Area of Science:

• Biomechanics
• Robotics
• Prosthetics

Background:

• Gait simulation faces challenges in achieving physiological velocities, full-scale ground reaction forces, and multi-planar motion.
• Existing systems often lack the capability to accurately replicate these complex gait parameters.

Purpose of the Study:

• To develop and validate a novel 6-degree of freedom parallel robotic gait simulator (RGS).
• To overcome key limitations in current gait simulation technologies for enhanced biomechanical research.

Main Methods:

• Development of a 6-degree of freedom parallel robot for gait simulation.
• Utilizing a prosthetic foot for initial system validation with recorded gait data from a transtibial amputee.
• Employing a proportional iterative learning controller to achieve accurate vertical ground reaction forces.

Main Results:

• The RGS successfully reproduced recorded kinematics and kinetics of gait using a prosthetic foot.
• The iterative learning controller reduced the root mean square error (RMSE) in vertical ground reaction force to 35 N after six iterations.
• The system demonstrated accurate simulation of the stance phase of gait over 1.5 seconds.

Conclusions:

• The developed robotic gait simulator (RGS) is a viable tool for accurate gait simulation, including physiological velocities and forces.
• The RGS shows promise for future studies with cadaveric specimens, advancing biomechanical analysis of locomotion.