Name
Abstract | ||
|---|---|---|
The tuning process for a robotic prosthesis is a challenging and time-consuming task both for users and clinicians. An automatic tuning approach using reinforcement learning (RL) has been developed for a knee prosthesis to address the challenges of manual tuning methods. The algorithm tunes the optimal control parameters based on the provided knee joint profile that the prosthesis is expected to replicate during gait safely. This paper presents an intuitive interface designed for the prosthesis users and clinicians to choose the preferred knee joint profile during gait and use the autotuner to replicate in the prosthesis. The interface-based approach is validated by observing the ability of the tuning algorithm to successfully converge to various alternate knee profiles by testing on two able-bodied subjects walking with a robotic knee prosthesis. The algorithm was found to converge successfully in an average duration of 1.15 min for the first subject and 2.31 min for the second subject. Further, the subjects displayed different preferences for optimal profiles reinforcing the need to tune alternate profiles. The implications of the results in the tuning of robotic prosthetic devices are discussed. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IROS51168.2021.9636264 | 2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
DocType | ISSN | Citations |
Conference | 2153-0858 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abbas Alili | 1 | 0 | 0.34 |
| Varun Nalam | 2 | 0 | 0.68 |
| Minhan Li | 3 | 7 | 4.60 |
| Ming Liu | 4 | 23 | 3.37 |
| Jennie Si | 5 | 746 | 70.23 |
| He Huang | 6 | 315 | 43.94 |