Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Glenna L. Buford

dblp:28/11052 · DBLP profile ↗
← Back
3ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 3Artificial intelligence and machine learning · 2

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.

Human-computer interaction and pervasive computing
2 papers
Human-robot interaction · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
human-robot collaboration
0.322012
Human-human vs. human-robot teamed investigation · HRI 2012
Assessing workload in human-robot peer-based teams · HRI 2012
Human-robot interaction
workload assessment
0.112012
Assessing workload in human-robot peer-based teams · HRI 2012
Human-robot interaction
collaborative task
0.012012
Assessing workload in human-robot peer-based teams · HRI 2012

Methods — techniques the papers use, named apart from their topics

video-based evaluation · 0.1subjective workload rating · 0.1human-subject evaluation · 0.1first-responder scenario · 0.1
YearPublicationVenuePosition
2015 Mental workload and task performance in peer-based human-robot teams
abstract
Successful human-robot (H-R) teams working with direct interaction and close coupling will have relationships that vary, depending on whether or not collaboration is prioritized and how collaboration changes the human mental workload and the team's task performance. Modeling of representative functions can provide predictions of changes in mental workload and the potential impact on team performance. The presented research focuses on modeling and quantifying mental workload for H-R teams in which team members have some individually assigned responsibilities but must also make joint decisions with a teammate. IMPRINT Pro, a discrete event simulation modeling tool created by the U.S. Army Research Laboratory, was used to model human-human (H-H) and H-R teams completing a reconnaissance task in a building. This research evaluated H-H and H-R teams completing the same reconnaissance tasks. Predictions of mental-workload levels from the model and the evaluation results showed that mental workload was lower for the H-R teams. The results for the closely coupled teams were compared to results from a prior evaluation with a master-slave relationship; similar results were found for both evaluations. Mental workload was lower in H-R teams than in H-H teams, but task performance did not differ between the two.
Caroline E. Harriott, Glenna L. Buford, Julie A. Adams, Tao Zhang 0002
J. Hum. Robot Interact.2
2012 Assessing workload in human-robot peer-based teams
abstract
The effect of a robotic teammate on a human partner's workload has not been fully quantified. Prior research found that human participants experienced lower workload when working with a robotic partner than when working with a human partner. An evaluation investigated whether a similar trend in workload exists for tasks requiring direct and collaborative interaction between the partners, and joint team decision-making. The subjective results indicate a similar trend to the prior results; participants rated workload lower for the more complex task when partnered with a robot than when partnered with a human.
Caroline E. Harriott, Glenna L. Buford, Tao Zhang 0002, Julie A. Adams
HRI2
2012 Human-human vs. human-robot teamed investigation
abstract
Clips from an evaluation where participants, each paired with either a human or robot partner, were deployed to search a hallway for suspicious items, in a manner similar to tactics used by first responders handling bomb threats are presented. The teams used natural, verbal communication to collaborate, determine where hazards were located, and which items were suspicious. The video demonstrates that the investigations in both conditions played out in a similar manner and participants were able to complete the investigations successfully with a robot partner; however, sometimes the participants were uncertain how to interact with the robot.
Caroline E. Harriott, Glenna L. Buford, Tao Zhang 0002, Julie A. Adams
HRI2