Catherine Liu  ·  Claremont McKenna College

Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26), Barcelona, Spain

Hueman is a color-forward emotion tracking and management system that learns from each user's unique color–emotion associations. Rather than assuming universal mappings between color and feeling, Hueman treats color as a deeply personal medium — shaped by individual experience, cultural context, and situational meaning.

Through a two-part onboarding process, users create their own color–emotion map, which Hueman uses to generate predictive emotion label suggestions and adapt mobile interface color themes toward user-specified emotional states. As users continue logging, the system iteratively refines its model, keeping mappings dynamic and personal.

A weeklong deployment study with seven participants found that engaging with personalized color–emotion associations prompted meaningful reflection: surfacing implicit preferences users hadn't consciously recognized, and encouraging them to question why certain colors evoke certain feelings. Color-adaptive interfaces showed the most impact for calm, low-arousal target states, and worked best when a color carried a single, unambiguous emotional meaning for that user.