Development of a remote accessibility assessment system through three-dimensional reconstruction technology
Bibliographic record
Abstract
A Remote Accessibility Assessment System (RAAS) that uses three-dimensional (3-D) reconstruction technology is being developed; it enables clinicians to assess the wheelchair accessibility of users' built environments from a remote location. The RAAS uses commercial software to construct 3-D virtualized environments from photographs. We developed custom screening algorithms and instruments for analyzing accessibility. Characteristics of the camera and 3-D reconstruction software chosen for the system significantly affect its overall reliability. In this study, we performed an accuracy assessment to verify that commercial hardware and software can construct accurate 3-D models by analyzing the accuracy of dimensional measurements in a virtual environment and a comparison of dimensional measurements from 3-D models created with four cameras/settings. Based on these two analyses, we were able to specify a consumer-grade digital camera and PhotoModeler (EOS Systems, Inc, Vancouver, Canada) software for this system. Finally, we performed a feasibility analysis of the system in an actual environment to evaluate its ability to assess the accessibility of a wheelchair user's typical built environment. The field test resulted in an accurate accessibility assessment and thus validated our system.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".