Exploring the use of smartphone applications during navigation-based tasks for individuals who are blind or who have low vision: future directions and priorities
Bibliographic record
Abstract
PURPOSE: Mainstream smartphone applications are increasingly replacing the use of traditional visual aids to facilitate independent travel for people with blindness or low vision. However, little is known about which navigation apps are being used, the factors underpinning these decisions and why apps are not used in certain contexts. The goal of this study was to explore the navigation-based apps used by individuals who are blind or who have low vision, the factors influencing these decisions, and perceptions about gaps to address future needs in navigation. MATERIALS AND METHODS: An international online survey was conducted with 139 participants who self-identified as blind or low vision. RESULTS: Findings indicate that the decision to use an app based on artificial intelligence (AI) versus live video assistance is related to whether the task is dynamic or static in nature. Although most participants rely on apps only during unfamiliar routes (60.9%), apps are shown to supplement rather than replace traditional tools such as the white cane and dog guide. Participants underscore the need for future apps to better assist with indoor navigation and to provide more precise information about points of interest (POI). CONCLUSION: These results provide vital insights for technology developers about the perceived utility of smartphone apps for people with low vision or blindness during navigation. Our results highlight the importance of built-in accessibility features for users with visual impairments. As additional technology-based solutions are developed, it is essential that blind and low vision users, including rehabilitation professionals, are meaningfully included within design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".