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Online estimation algorithm for a biaxial ankle kinematic model with configuration dependent joint axes
1Department of Mechanical Engineering, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
Journal of Biomechanical Engineering
|February 2, 2011
Summary
This study developed an online algorithm using a modified recursive least-squares (RLS) method to accurately estimate human ankle kinematics. The approach accounts for variations in joint axis orientations, crucial for adaptive control in ankle rehabilitation robots.
Area of Science:
- Biomechanics
- Robotics
- Rehabilitation Engineering
Background:
- Human ankle kinematics are often simplified to a biaxial hinge model.
- Significant inter-individual and inter-foot configuration variations exist in ankle joint axis orientations.
- Accurate kinematic parameters are vital for adaptive control in ankle rehabilitation robots.
Purpose of the Study:
- To develop an online algorithm for estimating human ankle kinematic parameters.
- To address variations in ankle joint axis orientations.
- To enhance adaptive control in ankle rehabilitation robots.
Main Methods:
- Developed an extended biaxial ankle kinematic model allowing variable axis orientations.
- Implemented an online parameter estimation routine based on the recursive least-squares (RLS) algorithm.
- Utilized a modified RLS algorithm that penalizes parameter deviations from nominal values.
Main Results:
- Simulation results demonstrated the extended model's effectiveness in capturing foot orientation with variable joint axes.
- Experimental results confirmed the modified RLS algorithm yields realistic parameter estimates.
- The modified RLS algorithm maintained estimation accuracy comparable to conventional RLS.
Conclusions:
- The developed online estimation algorithm effectively captures human ankle kinematics, even with varying joint axes.
- The modified RLS approach provides more realistic parameter estimates for ankle rehabilitation robots.
- This research advances adaptive control strategies for personalized robotic rehabilitation.
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