Name
Title | ||
|---|---|---|
Semi-autonomous Robot-assisted Cooperative Therapy Exercises for a Therapist's Interaction with a Patient |
Abstract | ||
|---|---|---|
Recent increases in demand for post-stroke motor rehabilitation services together with limited time of therapist and accessibility issues, in particular for patients living in remote areas, have created a significant burden on healthcare systems worldwide. Semi-autonomous techniques that allow for sharing the time of a therapist between multiple patients have attracted great interest. Among them Learning from Demonstration (LfD) based robots have been studied as solutions to address this growing demand. In this work, a Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) based LfD approach are proposed to generate a versatile framework to deliver rehabilitation in the absence of the therapist. To collect data for training the models, a bilateral telerehabilitation system is used to enable patient-therapist collaborative task performance is one Degree of Freedom (DOF). The performance and generalizability of the trained model are demonstrated for a variety of patient actions. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/GlobalSIP45357.2019.8969143 | IEEE Global Conference on Signal and Information Processing |
DocType | ISSN | Citations |
Conference | 2376-4066 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Martinez | 1 | 0 | 1.01 |
| Jason Fong | 2 | 2 | 2.76 |
| Seyed Farokh Atashzar | 3 | 40 | 13.16 |
| Mahdi Tavakoli | 4 | 223 | 49.03 |