Evidence map›Paper›PMID 39850496›Full record

ArticleIUI. International Conference on Intelligent User Interfaces2022

Opportunities for Human-AI Collaboration in Remote Sighted Assistance.

Sooyeon Lee, Rui Yu, Jingyi Xie, Syed Masum Billah, John M Carroll

Abstract read
In one paragraph

Article in IUI. International Conference on Intelligent User Interfaces, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. BubbleCam: Engaging Privacy in Remote Sighted Assistance.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024
    Article
  6. Article
  7. Review
  8. Iterative Design and Prototyping of Computer Vision Mediated Remote Sighted Assistance.ACM transactions on computer-human interaction : a publication of the Association for Computing Machinery · 2022
    Article
  9. Article
  10. Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Models.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sooyeon LeePennsylvania State University, USA.
Rui YuPennsylvania State University, USA.
Jingyi XiePennsylvania State University, USA.
Syed Masum BillahPennsylvania State University, USA.
John M CarrollPennsylvania State University, USA.

Funding

SCH: INT: Conversations for Vision: Human-Computer Synergies in Prosthetic Interactions R01LM013330 · NLM · PENNSYLVANIA STATE UNIVERSITY, THE · PI CARROLL, JOHN MILLAR · 2019 to 2021
$713k
NLM NIH HHS R01 LM013330
6 · The paper itself

Abstract

Remote sighted assistance (RSA) has emerged as a conversational assistive technology for people with visual impairments (VI), where remote sighted agents provide realtime navigational assistance to users with visual impairments via video-chat-like communication. In this paper, we conducted a literature review and interviewed 12 RSA users to comprehensively understand technical and navigational challenges in RSA for both the agents and users. Technical challenges are organized into four categories: agents' difficulties in orienting and localizing the users; acquiring the users' surroundings and detecting obstacles; delivering information and understanding user-specific situations; and coping with a poor network connection. Navigational challenges are presented in 15 real-world scenarios (8 outdoor, 7 indoor) for the users. Prior work indicates that computer vision (CV) technologies, especially interactive 3D maps and realtime localization, can address a subset of these challenges. However, we argue that addressing the full spectrum of these challenges warrants new development in Human-CV collaboration, which we formalize as five emerging problems: making object recognition and obstacle avoidance algorithms blind-aware; localizing users under poor networks; recognizing digital content on LCD screens; recognizing texts on irregular surfaces; and predicting the trajectory of out-of-frame pedestrians or objects. Addressing these problems can advance computer vision research and usher into the next generation of RSA service.

Indexed as

3D mapsartificial intelligenceaugmented realitycameracomputer visionconversational assistancenavigationpeople with visual impairments blindremote sighted assistanceRSAsmartphone

Identifiers

PMID39850496
PMCPMC11755352

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.