Eccentric Visor: A User-Centered Mobile Application to Facilitate Reading and Vision Training for Individuals With Central Vision Loss
Bibliographic record
Abstract
Introduction: Central vision loss significantly impairs reading ability, and few technological tools aim to support both reading and eccentric viewing training. This study aimed to develop a user-centered mobile application, Eccentric Visor, to enhance reading accessibility and support eccentric vision training using evidence-based methods and computer vision techniques. Methods: The application was developed through an iterative, user-centered co-design process to produce a Minimum Viable Product (MVP). Key features include customizable text presentation and a visual fixation marker to support the steady eye strategy. Usability and acceptability were evaluated through structured questionnaires and open-ended feedback from individuals with central vision loss and low vision rehabilitation professionals. Results: The MVP incorporated reading enhancement strategies such as font and contrast adjustments and dynamic scrolling text. Most users found the application easy to use and effective for practicing eccentric viewing. All professionals indicated they would recommend the app in clinical contexts, highlighting its utility as both a reading aid and a potential training tool. Conclusion: Eccentric Visor shows promise as a digital resource that may support both accessible reading and eccentric viewing training. Preliminary findings suggest the app is usable, adaptable, and well-received by users and clinicians. It may also serve as a platform for future enhancements and formal efficacy studies. Implications for practitioners: By integrating customizable features with evidence-based design, Eccentric Visor may offer rehabilitation professionals a practical tool to support independent reading and ongoing visual training.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".