EmoFlow: From Tracking to Sense-Making of Emotions Through Creative Drawing

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Authors: Shannon Sie Santosa, Qian Wan, Junnan Yu, Yuhan Luo
Cited by: 1
References: 64

Abstract

While previous research has attempted to link features of individuals’ drawings to their emotional states, it often overlooks the deeply personal and context-driven nature of visual expression. To bridge the gap, we conducted a two‑week diary study with 21 participants, who used a custom‑built app to track daily emotions through free drawings, followed by interviews reflecting on their artwork. Among the 252 drawings gathered, we found no strong correlations between reported emotions and measurable drawing behaviors; instead, participants expressed emotions through diverse approaches, from illustrations of emotion sources (e.g., events, objects) and metaphors, to emojis, literal text and spontaneous, random mark-making. Participants developed consistent personal styles and described drawing as an intuitive, playful, and safe outlet, though some faced challenges with the ambiguity of visual expressions and interpreting their creations afterwards. With the lessons learned, we discuss opportunities for designing expression-centered emotion tracking technologies that embrace individuality and creativity.

Fields of Study & Subjects

Innovative Human-Technology Interaction Digital Communication and Language Emotion and Mood Recognition

Publication details

Journal / source
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Pages
1–18
Publication date
13 April 2026
Language
EN