Toward improving internet navigation for visually impaired screen Reader users: Co-designing an open-source assistive technology system
Bibliographic record
Abstract
Visually impaired individuals, estimated at 285 million globally, rely heavily on-screen readers for internet access. However, much of the visually available information, such as the relationship between webpage elements, does not translate well to its textual representation and must be always kept in memory, limiting contextual interactions. To address this challenge, we developed Touch Matrix Assistive Technology Navigator (TOMAT), an open-source system that works alongside screen readers to provide an interactive, audio-tactile representation of webpage structure and enable contextual interactions. Our study employed a participatory design approach, involving visually impaired users, healthcare professionals, engineers, and community organizations in co-design sessions, prototype demonstrations, and focus groups. The resulting system extracts and presents non-linear web information at multiple levels of detail, allowing users to dynamically adjust granularity and efficiently navigate and interact with web content. Participants reported that TOMAT enhanced their understanding of webpage structure and provided an intuitive complement to screen reader software. The findings suggest TOMAT has the potential to improve the internet navigation experience for visually impaired users, fostering greater independence and digital participation. To support further development and collaboration, TOMAT's source files have been released under an open-source license.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".