A Novel Multifunctional Assistive Device for Enhancing Mobility and Social Distancing Compliance among Visually Impaired Individuals
Bibliographic record
Abstract
The global population of visually impaired individuals continues to rise, as reported by the World Health Organization (WHO).The widespread prevalence of visual impairments necessitates innovative solutions for overcoming challenges related to spatial navigation and obstacle avoidance.This study introduces a multifunction assistive device, designed to augment safety measures and mobility for visually impaired individuals by acting as an artificial vision unit that issues cautionary alerts.The device is comprised of an array of sensors, microcontrollers, and wearable gadgets, providing immediate assistance to its users.Three primary components form the system: a smart glove, a smart shoe, and a smart hat.Insight obstacles are detected by the smart glove, which subsequently alerts the user.Ground obstacles, slopes, and wet surfaces are identified by the smart shoe.The smart hat is equipped with features that facilitate social distancing, maintaining a minimum distance of 2 meters to prevent viral infection, such as that of the Coronavirus, and enhance visibility at night.Users receive system notifications through sound or vibration.System evaluation demonstrated a high detection accuracy ranging from 95% to 99% in ambient obstacle detection, ensuring efficient obstacle avoidance and enhanced personal safety.Furthermore, the equipment effectively alerted users to maintain a safe social distance during the COVID-19 epidemic.A comparative analysis with existing assistive systems revealed that the proposed system offers a broad spectrum of functionalities, including obstacle collision avoidance, flame detection, water collision avoidance, hole detection, day and night visibility, and social distancing alerts.The proposed assistive device represents a practical, affordable, and transportable solution that empowers visually impaired individuals by promoting independence and enhancing overall mobility.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".