Name
Abstract | ||
|---|---|---|
A critical question to be answered to improve robotic rehabilitation is what is the optimal rehabilitation environment for a subject that will facilitate maximum recovery during therapy? Studies suggest that task variability, nature and degree of assistance or error-augmentation and type of feedback play a critical role in motor (re)-learning. In this work, we present a framework for robot-assisted motor (re)-learning that provides subject-specific training by allowing for simultaneous adaptation of task, assistance and feedback based on the performance of the subject on the task. We model a continuous and coordinated multi-joint task using a learning-from-demonstration approach, which allows the task to be modeled in a generative manner such that the challenge-level of the task could be modulated in an online manner. To train the subjects for dexterous manipulation, we present a torque-based task that requires the subject to dynamically regulate their joint torques. Finally, we carry out a pilot study with healthy human subjects using our previously developed hand exoskeleton to test a hypothesis and the results suggest that training under simultaneous adaptation of task, assistance and feedback positively affects motor learning. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICRA.2017.7989061 | 2017 IEEE International Conference on Robotics and Automation (ICRA) |
Keywords | Field | DocType |
optimization,robotic exoskeleton,robotic rehabilitation,optimal rehabilitation environment,therapy,robot-assisted motor relearning,subject-specific training,feedback,continuous multijoint task,coordinated multijoint task,learning-from-demonstration approach,dexterous manipulation,torque | Rehabilitation,Torque,Motor learning,Robot kinematics,Control engineering,Medical treatment,Exoskeleton,Engineering,Powered exoskeleton,Dexterous manipulation | Conference |
Volume | Issue | ISBN |
2017 | 1 | 978-1-5090-4634-8 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Priyanshu Agarwal | 1 | 43 | 9.60 |
| Ashish D. Deshpande | 2 | 42 | 11.33 |