Name
Abstract | ||
|---|---|---|
Powered hand prostheses with many degrees of freedom are moving from research into the market for prosthetics. In order to make use of the prostheses' full functionality, it is essential to find efficient ways to control their multiple actuators. Human subjects can rapidly learn to employ electromyographic (EMG) activity of several hand and arm muscles to control the position of a cursor on a computer screen, even if the muscle-cursor map contradicts directions in which the muscles would act naturally. We investigated whether a similar control scheme, using signals from four hand muscles, could be adopted for real-time operation of a dexterous robotic hand. Despite different mapping strategies, learning to control the robotic hand over time was surprisingly similar to the learning of two-dimensional cursor control. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6610968 | 2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
robots,real time systems,tuning,biomechanics,degrees of freedom | Computer vision,Robotic hand,Computer science,Medical robotics,Hand muscles,Electromyography,Artificial intelligence,Cursor (user interface) | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 3 |
PageRank | References | Authors |
0.60 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tobias Pistohl | 1 | 28 | 3.48 |
| Christian Cipriani | 2 | 3 | 0.60 |
| A Jackson | 3 | 30 | 4.43 |
| Kianoush Nazarpour | 4 | 3 | 1.27 |