Individualized Three-Dimensional Gait Pattern Generator for Lower Limbs Rehabilitation Robots.

Area of Science:

• Robotics
• Biomechanics
• Rehabilitation Engineering

Background:

• Current gait generator models are primarily designed for exoskeletons, limiting their application in end-effector robotic rehabilitation devices.
• End-effector robots require control over both orientation and 3D position of subject joints, which existing models do not easily accommodate.

Purpose of the Study:

• To propose a novel, individualized 3D gait pattern generator specifically for end-effector gait rehabilitation robots.
• To enable the implementation of human-like gait trajectories in robotic rehabilitation devices that control 3D joint positions.

Main Methods:

• Utilized multi-variable regression models to predict joint angular trajectories (pelvis, hip, ankle) throughout the gait cycle.
• Reconstructed 3D joint positions using inverse kinematics applied to a human model based on the inverted pendulum analogy.
• Statistically evaluated the generator's performance against gait data from 42 participants across 8 different walking velocities.

Main Results:

• The proposed generator accurately predicted joint trajectories, achieving an average Root Mean Squared Error of 25.73 mm across all joints and Cartesian axes.
• Optimal performance was observed at walking velocities between 3.3 and 5.4 km/h.
• The model demonstrated a strong match with measured human gait patterns.

Conclusions:

• The developed 3D gait pattern generator is a suitable solution for end-effector gait robotic rehabilitation devices.
• The generator's ability to accurately predict and reconstruct human-like 3D gait trajectories enhances the potential for personalized rehabilitation.
• This approach advances the capabilities of robotic systems in delivering effective gait rehabilitation.