An Adaptable Human-Like Gait Pattern Generator Derived From a Lower Limb Exoskeleton.

Area of Science:

• Robotics
• Biomechanics
• Rehabilitation Engineering

Background:

• Conventional walking rehabilitation relies on repetitive movements and therapist expertise.
• Current lower-limb exoskeletons lack adaptability to individual patient biomechanics.
• Significant advancements are needed for robotic systems to provide effective clinical gait rehabilitation.

Purpose of the Study:

• To develop a method for acquiring and analyzing subject-specific gait data with a passive lower-limb exoskeleton.
• To generate adjustable, subject-specific kinematic gait trajectories for robotic rehabilitation protocols.
• To provide a benchmark for evaluating the human-likeness of robotic gait trajectories.

Main Methods:

• Captured kinematic data from ten healthy subjects walking with a lower-limb exoskeleton under three step-length conditions.
• Normalized ankle trajectories in the sagittal plane from heel strike to heel strike.
• Utilized six key gait cycle events (Heel Strike, Toe Off, Pre-Swing, Initial Swing, Mid-Swing, Terminal Swing) for linear regression analysis.
• Employed Leave-One-Out Cross Validation to assess trajectory fitting accuracy.

Main Results:

• A method was developed to capture and analyze subject-specific gait data.
• Generated ankle trajectories demonstrated high fidelity (average 95.2-97.2%) compared to captured data across different step lengths.
• The method allows for the generation of adjustable, human-like gait trajectories.

Conclusions:

• The proposed method enables the creation of subject-specific, adaptable gait trajectories for robotic rehabilitation.
• These trajectories can be scaled, computed online, and adjusted for various gait scenarios.
• This approach offers a valuable tool for programming gait controllers and benchmarking robotic exoskeletons.