Adaptive systems for color blindness: A proposed model for color and contrast adjustment

Abstract

With estimates that color blindness affects approximately 8% of men and 0.5% of women globally, ensuring visual accessibility in digital interfaces emerges as a crucial challenge, significantly impacting the interaction of millions of people. This condition compromises usability and digital inclusion, highlighting the need for more adaptive and intuitive solutions. This article addresses this gap, evaluating the potential of adaptive color and contrast systems to enhance digital accessibility for colorblind individuals, optimizing human-digital system interaction from an ergonomic perspective. To this end, a qualitative methodology was employed, based on a literature review and comparative analysis of technological solutions, including color blindness simulation tools and the theoretical formulation of an adaptive model driven by Artificial Intelligence (AI). The results indicated limitations in existing tools regarding personalization, real-time adaptation, and precise identification of color blindness type. In response, a theoretical model is proposed, comprising a system for identifying the type and degree of color blindness, dynamic color and contrast adjustment algorithms, and a customizable user interface, aiming to overcome these restrictions and offer a more complete and tailored solution to individual needs. The main contribution of the article lies in the proposition of this adaptive model, which, by integrating precise identification and AI-based personalization, optimizes visual experience and promotes digital inclusion. This work opens new perspectives for the development of more equitable interfaces, serving as a benchmark for accessible design.