Visionary Guidance: Wearable Navigation System for Blind
Bibliographic record
Abstract
This paper presents a wearable navigational device intended for use by individuals with vision impairments as they move about outdoors independently and safely. The intent is to enable individuals by increasing both their confidence and mobility so they are able to explore their environment with greater freedom and independence. The system incorporates multiple high-tech approaches including ultrasonic and infrared sensors for real-time obstacle/proximity detection while a camera-based system provides a broad situational awareness. At the system’s core is a convolutional neural network (CNN) designed to classify and robustly perceive various elements of outdoor environments—pedestrians, vehicles and rough terrain. Sensor fusion with GPS enables it to track and map locations with accurate spatial information and construct a dense model of the environment, even in the presence of unexplored territory. User gesture recognition, real-time obstacle avoidance, and precise navigational mapping are all within its capabilities. It implements terrain and obstacle avoidance via tactile and auditory means, offering directional voice prompts and vibration cues regarding potential threats. The convenient hands free operation can be done using voice commands because of the streamlined design of the device. This device is most useful for users who are fully blind or otherwise visually impaired. The device’s ability to retain and enhance on-the-go navigation is also its distinctive functionality. The device utilizes algorithms in machine learning to refine its navigation accuracy continuously based on user behavior and environmental stimuli. Reliability and effectiveness were demonstrated in field tests conducted in various outdoor settings. These tests showed the system promotes independent travel for people with visual impairments. This work is aligned with other contemporary research in assistive technology and serves as a strong basis for future research in autonomous mobility aids.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".