A Scoping Review of Assistance and Therapy with Head-Mounted Displays for People Who Are Visually Impaired
Bibliographic record
Abstract
Given the inherent visual affordances of Head-Mounted Displays (HMDs) used for Virtual and Augmented Reality (VR/AR), they have been actively used over many years as assistive and therapeutic devices for the people who are visually impaired. In this article, we report on a scoping review of literature describing the use of HMDs in these areas. Our high-level objectives included detailed reviews and quantitative analyses of the literature, and the development of insights related to emerging trends and future research directions. Our review began with a pool of 1,251 articles collected through a variety of mechanisms. Through a structured screening process, we identified 61 English research articles employing HMDs to enhance the visual sense of people with visual impairments for more detailed analyses. Our analyses reveal that there is an increasing amount of HMD-based research on visual assistance and therapy, and there are trends in the approaches associated with the research objectives. For example, AR is most often used for visual assistive purposes, whereas VR is used for therapeutic purposes. We report on eight existing survey articles, and present detailed analyses of the 61 research articles, looking at the mitigation objectives of the researchers (assistive versus therapeutic), the approaches used, the types of HMDs, the targeted visual conditions, and the inclusion of user studies. In addition to our detailed reviews and analyses of the various characteristics, we present observations related to apparent emerging trends and future research directions.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".