An AI-Powered Intermediate Accessibility App for Visually Impaired Users with Real-Time Voice and Vibration Support
Bibliographic record
Abstract
This paper presents the design and implementation of an AI-powered, multilingual smart assistant application aimed at enhancing smartphone accessibility for visually impaired individuals. Acting as an intermediate control layer, the application enables complete hands-free operation of essential mobile applications including WhatsApp, YouTube, Contacts, and Maps through intuitive voice commands. The system integrates a suite of assistive technologies to improve user autonomy and safety: voice-based app navigation; real-time emotion recognition; object and product identification using OCR and barcode/QR scanning; and fall detection with emergency alerts. A key innovation lies in the integration of a compact wearable camera attached to the user’s clothing and connected via Bluetooth to the smartphone which captures images of the surrounding environment. These images are processed on the mobile device, enabling real-time feedback through speech synthesis. The application supports multilingual interaction in English, Tamil, and Hindi, enhancing accessibility for diverse user groups. Developed using the Flutter framework, the system ensures cross-platform compatibility and is optimized for devices with limited hardware capabilities. This work contributes a scalable and inclusive solution that leverages artificial intelligence and wearable technology to empower visually impaired users. By bridging the gap between standard mobile interfaces and assistive needs, the proposed system promotes independence, mobility, and digital equity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".