Spatiotemporal Compliance Control for a Wearable Lower Limb Rehabilitation Robot

Area of Science:

• Robotics
• Rehabilitation Engineering
• Control Systems

Background:

• Physical human-robot interaction is vital for safe and comfortable robot-assisted rehabilitation.
• Existing control strategies may not fully optimize user adaptation and interaction dynamics.

Purpose of the Study:

• To design and evaluate a spatiotemporal compliance control strategy for a wearable lower limb rehabilitation robot (WLLRR).
• To enable users to regulate their motion's spatial and temporal characteristics during rehabilitation.
• To adapt gait speed based on user motor performance.

Main Methods:

• Developed a WLLRR with a high-level trajectory planner (trajectory generator, interaction torque estimator, gait speed adaptive regulator) and a low-level radial basis function neural network adaptive controller.
• Conducted over-ground walking experiments with five healthy participants comparing passive control, spatial compliance control, and spatiotemporal compliance control.

Main Results:

• The spatiotemporal compliance control strategy enabled participants to adjust the reference trajectory via physical human-robot interaction.
• The strategy adaptively modified gait speed based on participant motor performance.
• Demonstrated enhanced motor variability and reduced interaction torque compared to other control strategies.

Conclusions:

• The spatiotemporal compliance control strategy offers significant potential for improving robot-assisted rehabilitation training.
• This control approach enhances user adaptation and interaction in physical human-robot interaction applications.