Data for Validating a Model of Architectural Hazard Visibility with Low-Vision Observers

The three text files "UoMRLVsubjScore", "UoMSLVSubjScore", and "UoMLVSubjScoreROI_Central" contain the Hazard Visibility Scores (HVS) generated by the DeVAS software for each trial based on the stimulus image and the subject's visual acuity (VA) and contrast sensitivity (CS). The first two text files used comprehensive ROI, while the third file used central ROI. "UoMRLVsubjScore" and "UoMLVSubjScoreROI_Central" were based on the low-vision subjects' vision conditions, and the "UoMSLVSubjScore" were based on the normal-sighted subjects. The software is available online via https://github.com/visual-accessibility/DeVAS-filter. The four CSV files, "SLVCSV_UoM_Mod", "SLVCSV_UoM_Sev", "RLVCSV_UoM", and "RLVCSV_ROI_Central" are the file used for logistic regression. "SLVCSV_UoM_Mod" contains the data from normally-sighted subjects wearing moderate blur goggles, and "SLVCSV_UoM_Sev" contains the data from normally-sighted subjects wearing severe blur goggle. "RLVCSV_UoM" contains the data from low-vision subjects. See the readme.txt file for more information.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 3-D 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 can predict the likelihood of observers correctly identifying hazards. We tested fourteen normally sighted subjects with blur goggles that reduced acuity to 1.2 logMAR or 1.6 logMAR and ten low-vision subjects with acuities ranging from 0.8 logMAR to 1.6 logMAR. Subjects viewed computer-generated images of a walkway containing five possible targets ahead—large step up, large 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. The software implementation of the model 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.NIH EY01783