Mediated Reality and Location Awareness to Facilitate Topographical Orientation
Bibliographic record
Abstract
Topographical orientation is the ability to orient oneself within the environment and to\nnavigate through it to specific destinations. Topographical disorientation (TD) refers to deficits in orientation and navigation in the real environment, and is a common sequela of brain injuries. People with TD often have difficulties interacting with and perceiving the surrounding environment. The literature suggests that patients with TD are likely to benefit from research leading to clinical standards of practice and technology to facilitate\ntopographical orientation.\n In the light of the above, the objectives of this thesis were to investigate methods of realizing a context-aware, wearable mediated environment system for indoor navigation, and to develop a standard method of quantifying the impact of such a system on indoor navigation task performance.\n In realizing these objectives, we first conducted an extensive literature review of in-door localization systems. This review served to identify potential technologies for an indoor, in-situ wayfinding assistive device. Subsequently, an automated navigation algorithm was designed. Our algorithm reduced the navigational effort of simulated patients with topographical disorientation while accounting for the physical abilities of the patient, environmental barriers and dynamic building changes. We introduced and demonstrated\na novel energy-based wayfinding metric, which is independent of route complexity. An\nexperiment was conducted to identify preferred graphical navigation tools for mediated reality wayfinding guidance. Different combinations of spatial knowledge, graphical presentations and reference frames were considered in the experiment. The data suggested that the locator and minimap are the preferred navigational tools. Two unique optical-inertial localization systems for real-time indoor human tracking were created. The first localization system was oriented to pedestrians, while the second was implemented on a wheelchair. Empirical tests produced localization accuracies comparable to those reported in literature. Finally, a fully operational mediated reality location aware system for indoor navigation was realized. Tests with human participants indicated a significant\nreduction in physical effort in comparison to the no-tool condition, during wayfinding\ntasks in an unfamiliar indoor environment. Collectively, the findings and developments\nof this thesis lay the foundation for future research on wearable, location-based navigational assistance for individuals with wayfinding difficulties.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".