Rubi Quinones

dblp:237/8453 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

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

Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1

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
1 paper
Human-robot interaction · 44% Interaction techniques and input · 44% Design research and methods · 13%

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

TopicWeightPapersLastEvidence papers
Interaction techniques and input
gesture elicitation
0.412019
Learning from Users: an Elicitation Study and Taxonomy for Communicating Small Unmanned Aerial System States Through Gestures · HRI 2019
Human-robot interaction › aerial robot interaction
human-drone interaction
0.412019
Learning from Users: an Elicitation Study and Taxonomy for Communicating Small Unmanned Aerial System States Through Gestures · HRI 2019
Design research and methods
participatory design
0.112019
Learning from Users: an Elicitation Study and Taxonomy for Communicating Small Unmanned Aerial System States Through Gestures · HRI 2019

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

gesture elicitation study · 0.4agreement scores · 0.4
YearPublicationVenuePosition
2019 Learning from Users: an Elicitation Study and Taxonomy for Communicating Small Unmanned Aerial System States Through Gestures
abstract
This paper presents a gesture set for communicating states to novice users from a small Unmanned Aerial System (sUAS) through an elicitation study comparing gestures created by participants recruited from the general public with varying levels of experience with an sUAS. Previous work in sUAS flight paths sought to communicate intent, destination, or emotion without focusing on concrete states such as Low Battery or Landing. This elicitation study uses a participatory design approach from human-computer interaction to understand how novice users would expect an sUAS to communicate states, and ultimately suggests flight paths and characteristics to indicate those states. We asked users from the general public (N=20) to create gestures for seven distinct sUAS states to provide insights for human-drone interactions and to present intuitive flight paths and characteristics with the expectation that the sUAS would have general commercial application for inexperienced users. The results indicate relatively strong agreement scores for three sUAS states: Landing (0.455), Area of Interest (0.265), and Low Battery (0.245). The other four states have lower agreement scores, however even they show some consensus for all seven states. The agreement scores and the associated gestures suggest guidance for engineers to develop a common set of flight paths and characteristics for an sUAS to communicate states to novice users.
Justin W. Firestone, Rubi Quinones, Brittany A. Duncan
HRI2