Demo Data from EDBT 2026 / reviewers in the wild / expert
Kristin A. Farry
dblp:44/6795
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1996
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Robot manipulation · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 77% Wearable and physiological sensing · 23% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation
dexterous manipulation |
0.0 | 1 | 1996 | Myoelectric teleoperation of a complex robotic hand · IEEE Trans. Robotics Autom. 1996 |
Robotics › Robot manipulation › robotic hand
robotic hand control |
0.0 | 1 | 1996 | Myoelectric teleoperation of a complex robotic hand · IEEE Trans. Robotics Autom. 1996 |
Human-robot interaction
teleoperation |
0.0 | 1 | 1996 | Myoelectric teleoperation of a complex robotic hand · IEEE Trans. Robotics Autom. 1996 |
Wearable and physiological sensing
electromyography |
0.0 | 1 | 1996 | Myoelectric teleoperation of a complex robotic hand · IEEE Trans. Robotics Autom. 1996 |
Methods — techniques the papers use, named apart from their topics
pattern recognition · 0.0myoelectric signal processing · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1996 | Myoelectric teleoperation of a complex robotic handabstractTeleoperation continues to be a primary control mode in robotics applications, particularly for robots with complex hands. This paper details a novel method of teleoperation of complex anthropomorphic robotic hands: converting the myoelectric signal (generated by the operator's muscles during movement) into robot commands replicating the motion. Myoelectric prosthetic hands have used this user interface for over two decades; however, the feasibility of using this approach for commanding more than one degree-of-freedom, as in the pincher type grip in current myoelectric hands, has been in question. The research described in this paper addresses myoelectric control of NASA/Johnson Space Center's sixteen degree-of-freedom Utah/MIT Dextrous Hand for two grasping (key and chuck) options and three thumb motions (abduction, extension, and flexion). We discuss myoelectric signal processing approaches, data collection apparatus, and a realtime teleoperation implementation. We also present results in realtime discrimination of key and chuck grasps and offline discrimination of thumb motions. Our results include a 90% correct grasp selection rate and an 87% correct thumb motion selection, both using the myoelectric spectrum. Kristin A. Farry, Ian D. Walker, Richard G. Baraniuk |
IEEE Trans. Robotics Autom. | 1 |