AN OVERVIEW OF ASSISTIVE DEVICES FOR BLIND AND VISUALLY IMPAIRED PEOPLE
Bibliographic record
Abstract
Across the world, there are approximately 253 million people with vision impairments, and assistive devices have constantly been in demand.Advanced research has led to the development of numerous assistive devices for blind people and visually impaired people (VIP) to improve their quality of life.An overview of these different types of assistive devices such as canes, glasses, hats and gloves is presented in this survey.A FCBPSS (F: function, C: context, B: behaviour, P: principle, S: state, S: structure) architecture of visual impairment assistance system is preliminarily proposed to allow other researchers to design the assistive devices with the good experience and the high performance for blind people and VIPs in the future.As VIPs and blind people may have different behaviour patterns, a criterion for classifying different types of vision impairments is presented.Subsequently, we classify the substitutive senses for visual perception into five categories: vision enhancement, audition, somatosense, visual prosthesis, and olfactory and gustation.Two commonly used feedback forms, namely audition and vibration, are elaborated.Based on literature survey, we also present a summary prospective of the development of assistive devices: add more sensing and feedback modules, use the knowledge of perception mechanism and behaviour pattern as the design guideline and design more reliable validation experiments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".