Direction and Location Are Not Sufficient for Navigating in Nonrigid Environments: An Empirical Study in Augmented Reality
Bibliographic record
Abstract
Nonrigid environments, such as the human colon, present unique challenges in maintaining spatial orientation during navigation. This paper presents a design concept for presenting spatial information in an augmented reality (AR) display, together with results of an experiment conducted to evaluate the relative usefulness of three types of spatial information for supporting navigation and spatial orientation in a nonrigid environment. Sixteen untrained subjects performed a simulated colonoscopy procedure, using rigid and nonrigid colon models and six different AR displays comprising various combinations of direction, location, and shape information related to the scope inside the colon. Results showed that, unlike navigating in rigid environments, subjects took 44% longer to navigate the nonrigid environment and were less efficient, and suggested that it may be useful to train aspiring endoscopists in an equivalent rigid environment initially. A navigational aid presenting shape information was more beneficial than location or direction information for navigating in the nonrigid environment. Even though the AR navigational aid display did not speed up travel time, navigation efficiency and confidence in direction and location judgment for all subjects were improved. Subjectively, subjects preferred having shape information, in addition to position and direction information, in the navigational aid.
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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 | 0.002 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".