Mixed Reality Glasses with Picture-in-Picture Navigation for Patients with Homonymous Hemianopic Visual Field Loss
Bibliographic record
Abstract
BACKGROUND: The visual rehabilitation options for patients with homonymous hemianopia (HH) are limited. We developed prototype software for commercially available mixed reality glasses (MRG) to help patients with HH better navigate their visual environment. METHODS: Unlike virtual reality, mixed reality allows users to see the actual environment with superimposed holograms. We developed software on the Unity platform for the Microsoft HoloLens 2 MRG to enable real-time picture-in-picture navigation (PIPN). With PIPN, a miniaturized view of the visual environment is transposed into the intact visual field in patients with HH. WebSockets was used for real-time communication between the MRG and remote interface. External calibration controls were developed with the Streamlit web-based interface. In this crossover study, patients with HH were tested on a timed obstacle course with and without the MRG and asked to rate the utility of PIPN on a linear analog scale. RESULTS: We transposed 52 diagonal degrees of the full field as a picture-in-picture into the intact hemifield of patients with HH. Five patients with HH were tested and on average rated the MRG as 19.7% more helpful for ambulation ( P = 0.028). On average, walk times with the MRG were 6 seconds slower than walk times without the MRG, but this was not statistically significant. CONCLUSIONS: We developed working prototype software for PIPN on a commercially available MRG. PIPN is a viable rehabilitation option to improve ambulatory navigation for patients with HH and will continue improving with hardware and software advancements.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".