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Cited by 75Augmented Reality ApplicationsAssistive Technology in Communication and MobilityTactile and Sensory Interactions

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

An augmented reality sign-reading assistant for users with reduced vision

Open access
Huang, Jonathan, Kinateder, Max, Dunn, Matt J., Jarosz, Wojciech, Yang, Xing-Dong, Cooper, Emily A.·2019·PLOS ONE
DOI 10.1371/journal.pone.0210630PMC PMC6334915
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Abstract

People typically rely heavily on visual information when finding their way to unfamiliar locations. For individuals with reduced vision, there are a variety of navigational tools available to assist with this task if needed. However, for wayfinding in unfamiliar indoor environments the applicability of existing tools is limited. One potential approach to assist with this task is to enhance visual information about the location and content of existing signage in the environment. With this aim, we developed a prototype software application, which runs on a consumer head-mounted augmented reality (AR) device, to assist visually impaired users with sign-reading. The sign-reading assistant identifies real-world text (e.g., signs and room numbers) on command, highlights the text location, converts it to high contrast AR lettering, and optionally reads the content aloud via text-to-speech. We assessed the usability of this application in a behavioral experiment. Participants with simulated visual impairment were asked to locate a particular office within a hallway, either with or without AR assistance (referred to as the AR group and control group, respectively). Subjective assessments indicated that participants in the AR group found the application helpful for this task, and an analysis of walking paths indicated that these participants took more direct routes compared to the control group. However, participants in the AR group also walked more slowly and took more time to complete the task than the control group. The results point to several specific future goals for usability and system performance in AR-based assistive tools.

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Crossref · 10.1371/journal.pone.0210630

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Figures

Fig 1 Application screenshots. Left column: Original view of a user looking at two signs with normal vision (top row) and simulated visual impairment via Gaussian blur (bottom row). A white circle indicates the user’s virtual cursor. Note that the signs are no longer readable with visual impairment. Middle column: Green AR signs are shown placed over the physical signs, indicating that the application identified two signs with high certainty. In real-time, these signs would be flashing at 3 Hz. Right column: Text is displayed with enhanced visibility after a user selects an AR sign.
Fig 1 Application screenshots. Left column: Original view of a user looking at two signs with normal vision (top row) and simulated visual impairment via Gaussian blur (bottom row). A white circle indicates the user’s virtual cursor. Note that the signs are no longer readable with visual impairment. Middle column: Green AR signs are shown placed over the physical signs, indicating that the application identified two signs with high certainty. In real-time, these signs would be flashing at 3 Hz. Right column: Text is displayed with enhanced visibility after a user selects an AR sign.CC BY · PubMed Central
Fig 2 Subjective responses. Bars show median and interquartile range for ratings of ease (A), comfort (B) and confidence (C) in the two groups.
Fig 2 Subjective responses. Bars show median and interquartile range for ratings of ease (A), comfort (B) and confidence (C) in the two groups.CC BY · PubMed Central
Fig 3 Path analysis. (A) Lines show estimated paths taken by users in the control (blue) and AR (red) groups. The width of the hallway is illustrated for each group with two horizontal lines. Note that paths outside of the hallway suggest errors either in motion tracking or path alignment. (B, C) Bars show mean and standard error for walking speed and time to complete task. Note that walking speed excludes stationary periods.
Fig 3 Path analysis. (A) Lines show estimated paths taken by users in the control (blue) and AR (red) groups. The width of the hallway is illustrated for each group with two horizontal lines. Note that paths outside of the hallway suggest errors either in motion tracking or path alignment. (B, C) Bars show mean and standard error for walking speed and time to complete task. Note that walking speed excludes stationary periods.CC BY · PubMed Central

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Metadata

Authors
Huang, J., Kinateder, M., Dunn, M. J., Jarosz, W., Yang, X.-D., & Cooper, E. A.
Affiliations
  • Dartmouth College (US)
  • Cardiff University (GB)
Corresponding author
Huang, Jonathan
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2019-01-16
Volume
14
Issue
1
Pages
e0210630
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0210630
PMC
PMC6334915
OpenAlex
W2909795082
Cited by
75
References
25
Citations by year
2026: 6 · 2025: 12 · 2024: 9 · 2023: 8 · 2022: 9 · 2021: 16 · 2020: 11 · 2019: 4
FWCI
4.47
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
  • NPARC (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • Microsoft
  • Oculus
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
  • Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
Keywords
augmented reality, decreased vision, visual impairment, wayfinding, indoor navigation, head-mounted displays, text detection, text-to-speech, assistive technology, usability, route efficiency
MeSH terms
Virtual Reality, Female, Humans, Male, User-Computer Interface, Persons with Visual Disabilities, Visually Impaired Persons, Young Adult

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.