Demo Data from EDBT 2026 / reviewers in the wild / expert
Connor Courtien
dblp:345/1531
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2024
0000-0003-4224-425XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 2 since 2021
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 |
Accessibility and assistive technology · 86% Collaborative and social computing · 14% | |
| Artificial intelligence
1 paper |
Video understanding and tracking · 100% | |
| Computer graphics and multimedia
1 paper |
Audio and music processing · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Accessibility and assistive technology › assistive technology for visual impairment
assistive technology for blind and low-vision users |
0.8 | 1 | 2024 | Help Supporters: Exploring the Design Space of Assistive Technologies to Support Face-to-Face Help Between Blind and Sighted Strangers · CHI 2024 |
Accessibility and assistive technology › accessible multimedia
audio description |
0.7 | 1 | 2023 | Front Row: Automatically Generating Immersive Audio Representations of Tennis Broadcasts for Blind Viewers · UIST 2023 |
Collaborative and social computing › computer-supported cooperative work
mixed-ability collaboration |
0.2 | 1 | 2024 | Help Supporters: Exploring the Design Space of Assistive Technologies to Support Face-to-Face Help Between Blind and Sighted Strangers · CHI 2024 |
Computer vision › Video understanding and tracking › video analytics
sports video analysis |
0.2 | 1 | 2023 | Front Row: Automatically Generating Immersive Audio Representations of Tennis Broadcasts for Blind Viewers · UIST 2023 |
Audio and music processing
spatial audio |
0.2 | 1 | 2023 | Front Row: Automatically Generating Immersive Audio Representations of Tennis Broadcasts for Blind Viewers · UIST 2023 |
Methods — techniques the papers use, named apart from their topics
spatialized 3d audio · 2.0computer vision · 2.0research through design · 0.8mixed-ability study · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Help Supporters: Exploring the Design Space of Assistive Technologies to Support Face-to-Face Help Between Blind and Sighted StrangersabstractBlind and low-vision (BLV) people face many challenges when venturing into public environments, often wishing it were easier to get help from people nearby. Ironically, while many sighted individuals are willing to help, such interactions are infrequent. Asking for help is socially awkward for BLV people, and sighted people lack experience in helping BLV people. Through a mixed-ability research-through-design process, we explore four diverse approaches toward how assistive technology can serve as help supporters that collaborate with both BLV and sighted parties throughout the help process. These approaches span two phases: the connection phase (finding someone to help) and the collaboration phase (facilitating help after finding someone). Our findings from a 20-participant mixed-ability study reveal how help supporters can best facilitate connection, which types of information they should present during both phases, and more. We discuss design implications for future approaches to support face-to-face help. Yuanyang Teng, Connor Courtien, David A. Rios, Yves M. Tseng, Jacqueline Gibson, Maryam Aziz, Avery Reyna, Rajan Vaish, Brian A. Smith 0001 |
CHI | 2 |
| 2023 | Front Row: Automatically Generating Immersive Audio Representations of Tennis Broadcasts for Blind ViewersabstractBlind and low-vision (BLV) people face challenges watching sports due to the lack of accessibility of sports broadcasts. Currently, BLV people rely on descriptions from TV commentators, radio announcers, or their friends to understand the game. These descriptions, however, do not allow BLV viewers to visualize the action by themselves. We present Front Row, a system that automatically generates an immersive audio representation of sports broadcasts, specifically tennis, allowing BLV viewers to more directly perceive what is happening in the game. Front Row first recognizes gameplay from the video feed using computer vision, then renders players’ positions and shots via spatialized (3D) audio cues. User evaluations with 12 BLV participants show that Front Row gives BLV viewers a more accurate understanding of the game compared to TV and radio, enabling viewers to form their own opinions on players’ moods and strategies. We discuss future implications of Front Row and illustrate several applications, including a Front Row plug-in for video streaming platforms to enable BLV people to visualize the action in sports videos across the Web. Gaurav Jain, Basel Hindi, Connor Courtien, Xin Yi Therese Xu, Conrad Wyrick, Michael C. Malcolm, Brian A. Smith 0001 |
UIST | 3 |