Navigation in smart environments using mediated reality tools
Bibliographic record
Abstract
Topographical Disorientation (TD) is the lack or loss of orientation and navigation abilities. People living with TD face functional challenges in everyday situations. Smart mediated reality environments present potential solutions for cognitive conditions like TD. In this article, we introduce a novel mediated reality location aware environment. It was hypothesized that tools which offer different positional information affect the navigation performance of a user. The objective of this study was to investigate preferred assistive tools for indoor navigation for use in a proposed mediated reality wayfinding system. These tools may eventually be used to assist patients with TD. To this purpose, we designed a novel wayfinding metric that can be used in the assessment of navigation tasks similar to a scavenger hunt. This novel metric is based on a relative energy expenditure ratio and is independent of navigation route complexity. We investigated four sets of tools (minimap, locator, coordinate display and routing compass) that can be used in a smart mediated reality environment to provide relevant wayfinding information. These tools were designed using different combinations of spatial knowledge (landmark, route or survey), graphical presentation (compass, text, icon, top/side view) and reference frames (egocentric or allocentric). Each tool was evaluated objectively and subjectively. The locator and minimap tools emerged as preferred interfaces, providing the most relevant wayfinding information while minimizing energy expenditure during navigation tasks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".