Validating a model of architectural hazard visibility with low-vision observers

Pedestrians with low vision are at risk of injury when hazards, such as steps and posts, have low visibility. This study aims at validating the software implementation of a computational model that estimates hazard visibility. The model takes as input a photorealistic 3D rendering of an architectura...

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Veröffentlicht in:PloS one 2021-11, Vol.16 (11), p.e0260267
Hauptverfasser: Liu, Siyun, Liu, Yichen, Kersten, Daniel J, Shakespeare, Robert A, Thompson, William B, Legge, Gordon E
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container_issue 11
container_start_page e0260267
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creator Liu, Siyun
Liu, Yichen
Kersten, Daniel J
Shakespeare, Robert A
Thompson, William B
Legge, Gordon E
description Pedestrians with low vision are at risk of injury when hazards, such as steps and posts, have low visibility. This study aims at validating the software implementation of a computational model that estimates hazard visibility. The model takes as input a photorealistic 3D rendering of an architectural space, and the acuity and contrast sensitivity of a low-vision observer, and outputs estimates of the visibility of hazards in the space. Our experiments explored whether the model could predict the likelihood of observers correctly identifying hazards. In Experiment 1, we tested fourteen normally sighted subjects with blur goggles that simulated moderate or severe acuity reduction. In Experiment 2, we tested ten low-vision subjects with moderate to severe acuity reduction. Subjects viewed computer-generated images of a walkway containing five possible targets ahead-big step-up, big step-down, small step-up, small step-down, or a flat continuation. Each subject saw these stimuli with variations of lighting and viewpoint in 250 trials and indicated which of the five targets was present. The model generated a score on each trial that estimated the visibility of the target. If the model is valid, the scores should be predictive of how accurately the subjects identified the targets. We used logistic regression to examine the correlation between the scores and the participants' responses. For twelve of the fourteen normally sighted subjects with artificial acuity reduction and all ten low-vision subjects, there was a significant relationship between the scores and the participant's probability of correct identification. These experiments provide evidence for the validity of a computational model that predicts the visibility of architectural hazards. It lays the foundation for future validation of this hazard evaluation tool, which may be useful for architects to assess the visibility of hazards in their designs, thereby enhancing the accessibility of spaces for people with low vision.
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This study aims at validating the software implementation of a computational model that estimates hazard visibility. The model takes as input a photorealistic 3D rendering of an architectural space, and the acuity and contrast sensitivity of a low-vision observer, and outputs estimates of the visibility of hazards in the space. Our experiments explored whether the model could predict the likelihood of observers correctly identifying hazards. In Experiment 1, we tested fourteen normally sighted subjects with blur goggles that simulated moderate or severe acuity reduction. In Experiment 2, we tested ten low-vision subjects with moderate to severe acuity reduction. Subjects viewed computer-generated images of a walkway containing five possible targets ahead-big step-up, big step-down, small step-up, small step-down, or a flat continuation. Each subject saw these stimuli with variations of lighting and viewpoint in 250 trials and indicated which of the five targets was present. 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It lays the foundation for future validation of this hazard evaluation tool, which may be useful for architects to assess the visibility of hazards in their designs, thereby enhancing the accessibility of spaces for people with low vision.</description><subject>Access for the disabled</subject><subject>Acuity</subject><subject>Adult</subject><subject>Applications software</subject><subject>Architects</subject><subject>Architecture</subject><subject>Architecture and disabled persons</subject><subject>Biology and Life Sciences</subject><subject>Blindness</subject><subject>Computer and Information Sciences</subject><subject>Computer applications</subject><subject>Computer Simulation</subject><subject>Design</subject><subject>Engineering and Technology</subject><subject>Environmental aspects</subject><subject>Estimates</subject><subject>Female</subject><subject>Goggles</subject><subject>Hazard assessment</subject><subject>Hazard 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This study aims at validating the software implementation of a computational model that estimates hazard visibility. The model takes as input a photorealistic 3D rendering of an architectural space, and the acuity and contrast sensitivity of a low-vision observer, and outputs estimates of the visibility of hazards in the space. Our experiments explored whether the model could predict the likelihood of observers correctly identifying hazards. In Experiment 1, we tested fourteen normally sighted subjects with blur goggles that simulated moderate or severe acuity reduction. In Experiment 2, we tested ten low-vision subjects with moderate to severe acuity reduction. Subjects viewed computer-generated images of a walkway containing five possible targets ahead-big step-up, big step-down, small step-up, small step-down, or a flat continuation. Each subject saw these stimuli with variations of lighting and viewpoint in 250 trials and indicated which of the five targets was present. The model generated a score on each trial that estimated the visibility of the target. If the model is valid, the scores should be predictive of how accurately the subjects identified the targets. We used logistic regression to examine the correlation between the scores and the participants' responses. For twelve of the fourteen normally sighted subjects with artificial acuity reduction and all ten low-vision subjects, there was a significant relationship between the scores and the participant's probability of correct identification. These experiments provide evidence for the validity of a computational model that predicts the visibility of architectural hazards. It lays the foundation for future validation of this hazard evaluation tool, which may be useful for architects to assess the visibility of hazards in their designs, thereby enhancing the accessibility of spaces for people with low vision.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>34807929</pmid><doi>10.1371/journal.pone.0260267</doi><tpages>e0260267</tpages><orcidid>https://orcid.org/0000-0002-0198-3665</orcidid><orcidid>https://orcid.org/0000-0002-3742-1680</orcidid><oa>free_for_read</oa></addata></record>
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subjects Access for the disabled
Acuity
Adult
Applications software
Architects
Architecture
Architecture and disabled persons
Biology and Life Sciences
Blindness
Computer and Information Sciences
Computer applications
Computer Simulation
Design
Engineering and Technology
Environmental aspects
Estimates
Female
Goggles
Hazard assessment
Hazard identification
Hazards
Health risks
Humans
Lighting
Low visibility
Male
Medicine and Health Sciences
Modelling
Observers
Pedestrians
Physical Sciences
Proportional Hazards Models
Reduction
Safety and security measures
Safety equipment
Social Sciences
Software
Statistical analysis
Testing
Visibility
Vision
Vision, Low
Visual Acuity
Visually disabled persons
Young Adult
title Validating a model of architectural hazard visibility with low-vision observers
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