Leveraging proprioception to make mobile phones more accessible to users with visual impairments
Bibliographic record
Abstract
Accessing the advanced functions of a mobile phone is not a trivial task for users with visual impairments. They rely on screen readers and voice commands to discover and execute functions. In mobile situations, however, screen readers are not ideal because users may depend on their hearing for safety, and voice commands are difficult for a system to recognize in noisy environments. In this paper, we extend Virtual Shelves--an interaction technique that leverages proprioception to access application shortcuts--for visually impaired users. We measured the directional accuracy of visually impaired participants and found that they were less accurate than people with vision. We then built a functional prototype that uses an accelerometer and a gyroscope to sense its position and orientation. Finally, we evaluated the interaction and prototype by allowing participants to customize the placement of seven shortcuts within 15 regions. Participants were able to access shortcuts in their personal layout with 88.3% accuracy in an average of 1.74 seconds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".