Enhancing Mobility for the Visually Impaired with AI and IoT-Enabled Mobile Applications
Bibliographic record
Abstract
Developing a mobile application that assists visually impaired persons (VIPs) in navigating public transportation systems is significantly important for the quality of life of the growing number of VIPs in the world. The application should utilize real-time information about bus and train schedules, as well as the layout of transit stations, to provide step-by-step directions to users for reaching their desired destination. The app should be accessible to individuals with a range of visual impairments, featuring high-contrast colors, large fonts, and text-to-speech functionality. It should also provide alerts when the user is approaching their stop and can communicate with transit operators to request assistance if needed. This paper presents a comprehensive survey of the existing applications and systems available for VIP in transportation and other areas, finding out the limitations of these applications to propose new and efficient solutions, especially solutions involving Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) – evolved cutting edge technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".