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Updated: May 27, 2026

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Edge-Based Vision-Language Assistive System for the Visually Impaired: A Quantized VLM Approach.

Batyr Arystanbekov, Huseyin Atakan Varol, Adnan Yazici

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |May 25, 2026
    PubMed
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    This study introduces an on-device assistive system using vision-language models (VLMs) for visually impaired individuals. The wearable technology offers real-time scene interpretation, improving navigation and independence without internet dependency.

    Area of Science:

    • Artificial Intelligence
    • Assistive Technology
    • Computer Vision

    Background:

    • Cloud-based image captioning systems for the visually impaired face challenges like latency and limited contextual information.
    • Existing solutions often lack the real-time processing and on-device capabilities required for effective real-world navigation.

    Purpose of the Study:

    • To develop a novel, edge-deployable assistive system for visually impaired individuals.
    • To overcome the limitations of cloud-based solutions by enabling on-device, real-time scene interpretation.
    • To enhance user independence and interaction through accessible AI.

    Main Methods:

    • Integration of a quantized LLaVA-NeXT-13B vision-language model (VLM) with speech recognition (Whisper) and synthesis (PIPER).
    • Deployment on a wearable platform (NVIDIA Jetson Orin NX and Raspberry Pi) for self-contained operation.

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  • User-centered design with a button interface and Bluetooth audio output to minimize cognitive load.

    • Main Results:

    • Quantization of the VLM showed minimal accuracy loss on VizWiz-VQA (2.6%) and VQAv2 (0.9%).
    • User evaluations revealed a 25% increase in image identification accuracy compared to a baseline model.
    • The system achieved practical latency (4-5 seconds) and high usability scores.

    Conclusions:

    • The developed edge-deployable VLM system offers a viable, real-time solution for visually impaired individuals.
    • On-device processing significantly improves accessibility and independence by removing cloud reliance.
    • This work contributes to scalable, interpretable, and accessible AI-driven assistive technologies.