Towards Context-aware Support for Color Vision Deficiency: An Approach Integrating LLM and AR
This work addresses the challenge of offering task-specific, context-aware support for color vision deficiency, which is incremental as it builds on existing tools by adding contextual reasoning capabilities.
The paper tackled the problem of providing context-aware assistance for people with color vision deficiency by developing an application that integrates augmented reality and a multi-modal large language model, with preliminary experiments showing effectiveness and universality across five scenarios involving two users.
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.