Accuracy and Usability of Smartphone-Based Distance Estimation Approaches for Visual Assistive Technology Development
<italic>Goal:</italic> 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. <italic>Methods:</italic> We tested five s...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10414161/ |
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| Summary: | <italic>Goal:</italic> 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. <italic>Methods:</italic> 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). <italic>Results:</italic> 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. <italic>Conclusions:</italic> We provide key information that helps design reliable smartphone-based visual assistive technologies to enhance the functionality of PBLV. |
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| ISSN: | 2644-1276 |