A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait

Area of Science:

• Robotics
• Biomechanics
• Human-Robot Interaction

Background:

• Relative motion between humans and exoskeletons significantly impacts interaction efficiency, reliability, and safety.
• Quantitative assessment of this relative motion is often neglected in current research.

Purpose of the Study:

• To develop and validate a methodology for predicting human joint motion based on exoskeleton frame motion.
• To improve the understanding and quantification of human-exoskeleton relative motion.

Main Methods:

• A subject-specific skeletal model was combined with a lower limb exoskeleton (H2, Technaid) kinematic model.
• Kinematic constraints were imposed to link the human and exoskeleton models.
• Experiments were conducted on seven healthy subjects during treadmill walking for calibration and validation.

Main Results:

• The methodology achieved a prediction accuracy below 3.5° globally.
• Hip joint motion prediction accuracy was approximately 1.5°.
• This represents an improvement of up to 66% compared to methods assuming no relative motion.

Conclusions:

• The developed methodology accurately predicts human joint motion during exoskeleton use.
• Accounting for relative motion significantly enhances prediction accuracy.
• This work provides a foundation for more efficient, reliable, and safer human-robot interaction with exoskeletons.