What's that sound? Distance determination and aperture passage from ultrasound echoes
Bibliographic record
Abstract
PURPOSE: Individuals with visual impairment have difficulty detecting obstacles that are above waist height. A prototype device was developed to allow individuals to hear ultrasound reflections off environmental obstacles. The purpose of this research was to evaluate novices' ability to evaluate distance and pass through apertures using this device. METHOD: The first experiment evaluated the ability to judge the distance from a wall using the ultrasound system as compared to using auditory echolocation. The second examined time for passage, centreline accuracy and angle of rotation through different sized apertures. RESULTS: Distance judgement was found to be better with audified ultrasound than with auditory echolocation. When passing through apertures, audified ultrasound enabled centreline precision similar to that of vision, but individuals did not rotate their shoulders while passing through suggesting that more practice is necessary to combine perceptive information with proprioceptive action. CONCLUSIONS: The ability to judge distance and navigate through an environment with obstacles using a device which audifies ultrasound was shown to be better than using auditory echolocation, but not as effective as vision. This device will allow individuals with visual impairments to better detect and avoid environmental obstacles.
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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 | 0.010 |
| 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) | 0.002 | 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".