Rethinking the Grocery Store: Inclusive Wayfinding System for Visually Impaired Shoppers in Grocery Stores
Bibliographic record
Abstract
Many people with disabilities face considerable barriers while shopping in grocery stores. One such barrier is that they cannot find their way around easily, especially when they visit the grocery store for the first time and have not yet built a cognitive map in their memory. They may also experience delays in finding the right product or waiting for assistance from store employees, thus leading them to rely on family, friends, relatives, or volunteers to help them with their shopping. Problems start when these people are not available, in which case the individual is forced to cancel their visit to the grocery store and reschedule the trip. Grocery stores include many different zones and services, the aisles area being one of the main barriers to access for people with different disabilities. This area features many different sections such as canned goods, dry packaged goods, spices, drinks and snacks, baking supplies, baby items, cereals, cleaning products, pet supplies, and health and beauty items. For visually impaired individuals, however, it can be hard to reach these various sections and find the relevant products. The objective of this research is to design an inclusive and innovative wayfinding system in grocery stores for visually impaired shoppers in order to help them find the center zone, orient between different aisles, decide where to go, move easily between different sections, and select products with ease. The research approach will be based on the literature review and the application of the Delphi method.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".