A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis
The tuning of robotic prosthesis control is essential to provide personalized assistance to individual prosthesis users. Emerging automatic tuning algorithms have shown promise to ease the device personalization procedure. However, very few automatic tuning algorithms consider the user preference as...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10015106/ |
| _version_ | 1797804996844060672 |
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| author | Abbas Alili Varun Nalam Minhan Li Ming Liu Jing Feng Jennie Si He Huang |
| author_facet | Abbas Alili Varun Nalam Minhan Li Ming Liu Jing Feng Jennie Si He Huang |
| author_sort | Abbas Alili |
| collection | DOAJ |
| description | The tuning of robotic prosthesis control is essential to provide personalized assistance to individual prosthesis users. Emerging automatic tuning algorithms have shown promise to ease the device personalization procedure. However, very few automatic tuning algorithms consider the user preference as the tuning goal, which may limit the adoptability of the robotic prosthesis. In this study, we propose and evaluate a novel prosthesis control tuning framework for a robotic knee prosthesis, which could enable user preferred robot behavior in the device tuning process. The framework consists of 1) a User-Controlled Interface that allows the user to select their preferred knee kinematics in gait and 2) a reinforcement learning-based algorithm for tuning high-dimension prosthesis control parameters to meet the desired knee kinematics. We evaluated the performance of the framework along with usability of the developed user interface. In addition, we used the developed framework to investigate whether amputee users can exhibit a preference between different profiles during walking and whether they can differentiate between their preferred profile and other profiles when blinded. The results showed effectiveness of our developed framework in tuning 12 robotic knee prosthesis control parameters while meeting the user-selected knee kinematics. A blinded comparative study showed that users can accurately and consistently identify their preferred prosthetic control knee profile. Further, we preliminarily examined gait biomechanics of the prosthesis users when walking with different prosthesis control and did not find clear difference between walking with preferred prosthesis control and when walking with normative gait control parameters. This study may inform future translation of this novel prosthesis tuning framework for home or clinical use. |
| first_indexed | 2024-03-13T05:45:29Z |
| format | Article |
| id | doaj.art-20aa29f2ea9142e899c742cef4ac8d10 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:45:29Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-20aa29f2ea9142e899c742cef4ac8d102023-06-13T20:10:28ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-013189590310.1109/TNSRE.2023.323621710015106A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee ProsthesisAbbas Alili0https://orcid.org/0000-0002-7849-6881Varun Nalam1https://orcid.org/0000-0003-0837-1175Minhan Li2https://orcid.org/0000-0002-5234-0248Ming Liu3https://orcid.org/0000-0003-0998-4221Jing Feng4Jennie Si5https://orcid.org/0000-0002-0374-7404He Huang6https://orcid.org/0000-0001-5581-1423Department of Electrical and Computer Engineering, NC State University, Raleigh, NC, USANC State/UNC Joint Department of Biomedical Engineering, NC State University, Raleigh, NC, USANC State/UNC Joint Department of Biomedical Engineering, NC State University, Raleigh, NC, USANC State/UNC Joint Department of Biomedical Engineering, NC State University, Raleigh, NC, USADepartment of Psychology, NC State University, Raleigh, NC, USADepartment of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USANC State/UNC Joint Department of Biomedical Engineering, NC State University, Raleigh, NC, USAThe tuning of robotic prosthesis control is essential to provide personalized assistance to individual prosthesis users. Emerging automatic tuning algorithms have shown promise to ease the device personalization procedure. However, very few automatic tuning algorithms consider the user preference as the tuning goal, which may limit the adoptability of the robotic prosthesis. In this study, we propose and evaluate a novel prosthesis control tuning framework for a robotic knee prosthesis, which could enable user preferred robot behavior in the device tuning process. The framework consists of 1) a User-Controlled Interface that allows the user to select their preferred knee kinematics in gait and 2) a reinforcement learning-based algorithm for tuning high-dimension prosthesis control parameters to meet the desired knee kinematics. We evaluated the performance of the framework along with usability of the developed user interface. In addition, we used the developed framework to investigate whether amputee users can exhibit a preference between different profiles during walking and whether they can differentiate between their preferred profile and other profiles when blinded. The results showed effectiveness of our developed framework in tuning 12 robotic knee prosthesis control parameters while meeting the user-selected knee kinematics. A blinded comparative study showed that users can accurately and consistently identify their preferred prosthetic control knee profile. Further, we preliminarily examined gait biomechanics of the prosthesis users when walking with different prosthesis control and did not find clear difference between walking with preferred prosthesis control and when walking with normative gait control parameters. This study may inform future translation of this novel prosthesis tuning framework for home or clinical use.https://ieeexplore.ieee.org/document/10015106/Robotic knee prosthesishuman-in-the-loop optimizationuser preferencereinforcement learninguser-controlled interface |
| spellingShingle | Abbas Alili Varun Nalam Minhan Li Ming Liu Jing Feng Jennie Si He Huang A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis IEEE Transactions on Neural Systems and Rehabilitation Engineering Robotic knee prosthesis human-in-the-loop optimization user preference reinforcement learning user-controlled interface |
| title | A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis |
| title_full | A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis |
| title_fullStr | A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis |
| title_full_unstemmed | A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis |
| title_short | A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis |
| title_sort | novel framework to facilitate user preferred tuning for a robotic knee prosthesis |
| topic | Robotic knee prosthesis human-in-the-loop optimization user preference reinforcement learning user-controlled interface |
| url | https://ieeexplore.ieee.org/document/10015106/ |
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