{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hamilton-Fletcher G"],"funding":["NEI NIH HHS","National Institutes of Health","Research to Prevent Blindness to NYU Langone Health Department of Ophthalmology","U.S. Department of Defense Vision Research Program"],"pagination":["54-58"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10939328"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["5"],"pubmed_abstract":["<i>Goal:</i> Distance information is highly requested in assistive smartphone Apps by people who are blind or low vision (PBLV). However, current techniques have not been evaluated systematically for accuracy and usability. <i>Methods:</i> We tested five smartphone-based distance-estimation approaches in the image center and periphery at 1-3 meters, including machine learning (CoreML), infrared grid distortion (IR_self), light detection and ranging (LiDAR_back), and augmented reality room-tracking on the front (ARKit_self) and back-facing cameras (ARKit_back). <i>Results:</i> For accuracy in the image center, all approaches had <±2.5 cm average error, except CoreML which had ±5.2-6.2 cm average error at 2-3 meters. In the periphery, all approaches were more inaccurate, with CoreML and IR_self having the highest average errors at ±41 cm and ±32 cm respectively. For usability, CoreML fared favorably with the lowest central processing unit usage, second lowest battery usage, highest field-of-view, and no specialized sensor requirements. <i>Conclusions:</i> We provide key information that helps design reliable smartphone-based visual assistive technologies to enhance the functionality of PBLV."],"journal":["IEEE open journal of engineering in medicine and biology"],"pubmed_title":["Accuracy and Usability of Smartphone-Based Distance Estimation Approaches for Visual Assistive Technology Development."],"pmcid":["PMC10939328"],"funding_grant_id":["R01 EY034897","R01-EY034897","W81XWH2110615"],"pubmed_authors":["Chan KC","Liu M","Hudson TE","Hamilton-Fletcher G","Rizzo JR","Sheng D","Feng C"],"additional_accession":[]},"is_claimable":false,"name":"Accuracy and Usability of Smartphone-Based Distance Estimation Approaches for Visual Assistive Technology Development.","description":"<i>Goal:</i> Distance information is highly requested in assistive smartphone Apps by people who are blind or low vision (PBLV). However, current techniques have not been evaluated systematically for accuracy and usability. <i>Methods:</i> We tested five smartphone-based distance-estimation approaches in the image center and periphery at 1-3 meters, including machine learning (CoreML), infrared grid distortion (IR_self), light detection and ranging (LiDAR_back), and augmented reality room-tracking on the front (ARKit_self) and back-facing cameras (ARKit_back). <i>Results:</i> For accuracy in the image center, all approaches had <±2.5 cm average error, except CoreML which had ±5.2-6.2 cm average error at 2-3 meters. In the periphery, all approaches were more inaccurate, with CoreML and IR_self having the highest average errors at ±41 cm and ±32 cm respectively. For usability, CoreML fared favorably with the lowest central processing unit usage, second lowest battery usage, highest field-of-view, and no specialized sensor requirements. <i>Conclusions:</i> We provide key information that helps design reliable smartphone-based visual assistive technologies to enhance the functionality of PBLV.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2026-06-24T03:13:01.942Z","creation":"2026-06-24T03:07:11.49Z"},"accession":"S-EPMC10939328","cross_references":{"pubmed":["38487094"],"doi":["10.1109/OJEMB.2024.3358562"]}}