biostudies-literatureHamilton-Fletcher GNEI NIH HHSNational Institutes of HealthResearch to Prevent Blindness to NYU Langone Health Department of OphthalmologyU.S. Department of Defense Vision Research Program54-58https://www.ebi.ac.uk/biostudies/studies/S-EPMC10939328biostudies-literatureUnknown5<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.IEEE open journal of engineering in medicine and biologyAccuracy and Usability of Smartphone-Based Distance Estimation Approaches for Visual Assistive Technology Development.PMC10939328R01 EY034897R01-EY034897W81XWH2110615Chan KCLiu MHudson TEHamilton-Fletcher GRizzo JRSheng DFeng CfalseAccuracy and Usability of Smartphone-Based Distance Estimation Approaches for Visual Assistive Technology Development.<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.2024-01-01T00:00:00Z20242026-06-24T03:13:01.942Z2026-06-24T03:07:11.49ZS-EPMC109393283848709410.1109/OJEMB.2024.3358562