Towards Context-aware Support for Color Vision Deficiency: An Approach Integrating LLM and AR

TL;DR

This paper presents a novel AR and LLM-based application that offers context-aware, autonomous support for individuals with color vision deficiency, addressing limitations of existing tools by providing tailored assistance across various scenarios.

Contribution

It introduces an integrated AR and large language model system for context-aware support, enabling personalized assistance for color vision deficiency in diverse real-world situations.

Findings

Preliminary user experiments show effectiveness across multiple scenarios.

The system demonstrates universality in assisting different users.

The approach combines AR context capture with LLM reasoning for tailored support.

Abstract

People with color vision deficiency often face challenges in distinguishing colors such as red and green, which can complicate daily tasks and require the use of assistive tools or environmental adjustments. Current support tools mainly focus on presentation-based aids, like the color vision modes found in iPhone accessibility settings. However, offering context-aware support, like indicating the doneness of meat, remains a challenge since task-specific solutions are not cost-effective for all possible scenarios. To address this, our paper proposes an application that provides contextual and autonomous assistance. This application is mainly composed of: (i) an augmented reality interface that efficiently captures context; and (ii) a multi-modal large language model-based reasoner that serves to cognitize the context and then reason about the appropriate support contents. Preliminary user experiments with two color vision deficient users across five different scenarios have demonstrated the effectiveness and universality of our application.