Obstacle Detection and Assistance for Visually Impaired Individuals Using an IoT-Enabled Smart Blind Stick
Bibliographic record
Abstract
As technological advancements permeate various aspects of life, they offer renewed hope for individuals grappling with disabilities. This paper focuses on the visually impaired population, who face considerable challenges in mobility due to physiological or neurological conditions causing blindness. Despite a reliance on external aid, a growing preference for self-sufficiency is observed among these individuals. In response to this, a pioneering tool, the Smart Blind Stick (SBS), is proposed to alleviate their mobility-related difficulties. The SBS is an advanced adaptive tool, designed to address daily navigation challenges faced by visually impaired individuals. The device operates by identifying obstacles and accurately calculating their distances using an integrated system of an Arduino UNO controller, Viola Jones algorithm, ultrasonic and water sensors. The SBS is equipped with a camera and advanced ultrasonic sensors, along with enhanced coding systems, enabling users to detect objects and navigate through challenging terrains. The SBS distinguishes itself from conventional aids by serving as an autonomous navigation companion, alerting the user of potential hazards such as water bodies, walls, staircases, or uneven surfaces via a headset connected to their phone. This paper elaborates on the development, functionality, and anticipated impact of the SBS in fostering greater autonomy among visually impaired individuals.
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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 | 0.001 |
| Research integrity | 0.000 | 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".