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Kristin A. Farry

dblp:44/6795 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
dexterous manipulation
0.011996
Myoelectric teleoperation of a complex robotic hand · IEEE Trans. Robotics Autom. 1996
Robotics › Robot manipulation › robotic hand
robotic hand control
0.011996
Myoelectric teleoperation of a complex robotic hand · IEEE Trans. Robotics Autom. 1996
Human-robot interaction
teleoperation
0.011996
Myoelectric teleoperation of a complex robotic hand · IEEE Trans. Robotics Autom. 1996
Wearable and physiological sensing
electromyography
0.011996
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
YearPublicationVenuePosition
1996 Myoelectric teleoperation of a complex robotic hand
abstract
Teleoperation 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