Compliant gait assistance triggered by user intention.

Area of Science:

• Robotics
• Rehabilitation Engineering
• Biomechanics

Background:

• Lower-limb wearable robots offer potential for gait rehabilitation.
• Current systems often require manual initiation or lack intuitive user intention sensing.
• Assisting patients with neurological conditions like spinal cord injury and stroke necessitates advanced control strategies.

Purpose of the Study:

• To propose and evaluate an automatic gait initialization strategy for lower-limb wearable robots.
• To leverage user intention sensing through human-orthosis interaction torques and position deviation.
• To enhance gait assistance and dynamic stability during rehabilitation.

Main Methods:

• Developed an automatic strategy monitoring human-orthosis interaction torques and initial position deviation for gait initiation.
• Implemented a compliant control algorithm adapting joint stiffness based on interaction torques and trajectory deviations.
• Utilized average gait data from healthy subjects as a reference input.
• Tested the algorithm with five healthy subjects.

Main Results:

• The strategy successfully initiated gait automatically.
• The system demonstrated efficient maintenance of equilibrium during walking, even with external forces.
• The compliant control algorithm contributed to dynamic stability.

Conclusions:

• The proposed automatic gait initialization strategy is effective for lower-limb wearable robot-assisted rehabilitation.
• User intention sensing via interaction torques and position deviation is a viable approach for gait initiation.
• This preliminary study shows potential for assisting patients with incomplete spinal cord injury and stroke.