AIDEN: Design and Pilot Study of an AI Assistant for the Visually Impaired

TL;DR

AIDEN is a novel AI assistant that combines real-time object detection, scene description, OCR, and haptic guidance to improve autonomy for the visually impaired, addressing limitations of existing audio-based solutions.

Contribution

The paper introduces a hybrid AI system with a unique haptic guidance mechanism that enhances independence without relying solely on auditory feedback.

Findings

High user satisfaction in perceived ease of use and autonomy

Effective real-time object detection and scene understanding

Potential for improved daily usability over traditional methods

Abstract

This paper presents AIDEN, an artificial intelligence-based assistant designed to enhance the autonomy and daily quality of life of visually impaired individuals, who often struggle with object identification, text reading, and navigation in unfamiliar environments. Existing solutions such as screen readers or audio-based assistants facilitate access to information but frequently lead to auditory overload and raise privacy concerns in open environments. AIDEN addresses these limitations with a hybrid architecture that integrates You Only Look Once (YOLO) for real-time object detection and a Large Language and Vision Assistant (LLaVA) for scene description and Optical Character Recognition (OCR). A key novelty of the system is a continuous haptic guidance mechanism based on a Geiger-counter metaphor, which supports object centering without occupying the auditory channel, while privacy is preserved by ensuring that no personal data are stored. Empirical evaluations with visually impaired participants assessed perceived ease of use and acceptance using the Technology Acceptance Model (TAM). Results indicate high user satisfaction, particularly regarding intuitiveness and perceived autonomy. Moreover, the ``Find an Object'' achieved effective real-time performance. These findings provide promising evidence that multimodal haptic-visual feedback can improve daily usability and independence compared to traditional audio-centric methods, motivating larger-scale clinical validations.