Abstract
In this study, we validated a human-in-the-loop auto-tuner using machine learning to automatically customize powered knee prosthesis control parameters for an amputee subject in real time. The experimental powered knee prosthesis was controlled by a finite state impedance controller, which had 12 configurable impedance control parameters. Using adaptive dynamic programming with reinforcement learning while one transfemoral amputee subject walked with the powered knee prosthesis, the auto-tuner would interact with the human-prosthesis system and learn to configure the high dimension of control parameters. We tested 4 different initial conditions with one unilateral transfemoral amputee subject. The results showed that the auto-tuner discovered the control parameters that allowed amputee subject to generate normative knee kinematics in 3 out of 4 tuning sessions. For all test sessions, the averaged root-mean-square error of the knee kinematics relative to the normative knee kinematics decreased from 6.6 degrees to 4.6 degrees after tuning procedure.
| Original language | English (US) |
|---|---|
| Title of host publication | 2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-2 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781538643778 |
| DOIs | |
| State | Published - Jun 12 2018 |
| Event | 2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017 - Houston, United States Duration: Nov 5 2017 → Nov 8 2017 |
Other
| Other | 2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017 |
|---|---|
| Country | United States |
| City | Houston |
| Period | 11/5/17 → 11/8/17 |
ASJC Scopus subject areas
- Rehabilitation
- Artificial Intelligence
- Biomedical Engineering
- Control and Optimization
- Clinical Neurology