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Case Study: A Bio-Inspired Control Algorithm for a Robotic Foot-Ankle Prosthesis Provides Adaptive Control of Level
Uzma Tahir1, Anthony L Hessel1, Eric R Lockwood2
1Center for Bioengineering Innovation and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, United States.
Frontiers in Robotics and AI
|January 27, 2021
Summary
A new winding filament hypothesis (WFH) control algorithm for powered prostheses mimics muscle properties. This adaptive algorithm improves stair ascent performance and matches natural walking, demonstrating robust control for prosthetic limbs.
Area of Science:
- Biomechanical Engineering
- Robotics
- Human Physiology
Background:
- Powered ankle-foot prostheses enhance mobility but require sophisticated control.
- Existing control algorithms often lack inherent adaptation to varying loads and terrains.
- Biological muscles exhibit intrinsic adaptation through changes in stiffness with length and velocity.
Purpose of the Study:
- To develop a novel control algorithm for powered prostheses based on the winding filament hypothesis (WFH).
- To evaluate the robustness and adaptive capabilities of the WFH algorithm during level walking and stair ascent.
- To compare the WFH algorithm's performance against a commercial prosthesis controller and unimpaired individuals.
Main Methods:
- Developed a WFH-based control algorithm calculating ankle moments from virtual muscle length and activation.
- Implemented the WFH algorithm in a BiOM T2 powered ankle-foot prosthesis.
- Conducted a case study with two experienced users performing level walking at variable speeds and stair ascent.
Main Results:
- The WFH algorithm produced plantarflexion angles and ankle moments during level walking comparable to the stock controller and unimpaired individuals.
- During stair ascent, the WFH algorithm generated significantly larger plantarflexion angles (~5x) than the stock controller, matching unimpaired individuals.
- The algorithm demonstrated robust control across different activities and speeds with minimal sensing and no parameter adjustments.
Conclusions:
- The WFH-based control algorithm offers a proof-of-concept for adaptive prosthetic limb control by emulating intrinsic muscle properties.
- This approach provides robust and adaptable control for level walking and stair ascent, outperforming current stock controllers in challenging tasks.
- The WFH algorithm represents a promising direction for developing more intuitive and responsive powered prostheses.
