Smart Glove for Blind
Bibliographic record
Abstract
The paper aims to present a user-friendly device for the visually challenged individuals to aid them in their navigation. It is well known that the visually impaired people find it difficult to detect obstacles in and around their surroundings and solely depend for help, be it on other individual person or device such as white cane and dog which have now proved to be quite inefficient and outdated. The device designed is a portable, low powered version and cost-efficient hand glove that is used to facilitate the visually impaired individuals to overcome the lack of visual sense and to create awareness of the unforeseen obstacle while walking and performing their everyday tasks. The glove makes use of an ultrasonic sensor that uses vibration signals to notify the user about the upcoming hurdle. The ultrasonic sensor detects the hurdle that falls within a distance of 100 cm and alerts the user about it, through a vibration. In addition to this, pulse sensor is used to monitor the heart rate of the user. If any abnormality found, the user's guardian will be alerted through a text message. Thus, the overall objective of the device is to provide a convenient and a safe method for the visually challenged individuals to overcome their difficulties in daily life without relying on others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.031 |
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".