Full metadata
Title
Bayesian Optimization for Robot-Aided Rehabilitation: Adaptive Variable Impedance Control of a Wearable Ankle Robot
Description
This thesis presents a study on the user adaptive variable impedance control of a wearable ankle robot for robot-aided rehabilitation with a primary focus on enhancing accuracy and speed. The controller adjusts the impedance parameters based on the user's kinematic data to provide personalized assistance. Bayesian optimization is employed to minimize an objective function formulated from the user's kinematic data to adapt the impedance parameters per user, thereby enhancing speed and accuracy. Gaussian process is used as a surrogate model for optimization to account for uncertainties and outliers inherent to human experiments. Student-t process based outlier detection is utilized to enhance optimization robustness and accuracy. The efficacy of the optimization is evaluated based on measures of speed, accuracy, and effort, and compared with an untuned variable impedance controller during 2D curved trajectory following tasks. User effort was measured based on muscle activation data from the tibialis anterior, peroneus longus, soleus, and gastrocnemius muscles. The optimized controller was evaluated on 15 healthy subjects and demonstrated an average increase in speed of 9.85% and a decrease in deviation from the ideal trajectory of 7.57%, compared to an unoptimized variable impedance controller. The strategy also reduced the time to complete tasks by 6.57%, while maintaining a similar level of user effort.
Date Created
2023
Contributors
- Manoharan, Gautham (Author)
- Lee, Hyunglae (Thesis advisor)
- Berman, Spring (Committee member)
- Xu, Zhe (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
73 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.187806
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2023
Field of study: Electrical Engineering
System Created
- 2023-06-07 12:34:01
System Modified
- 2023-06-07 12:34:09
- 1 year 5 months ago
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