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

Paper Title

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

Publication Info

• Topic area: Emotion tracking through creative, visual self-expression.
• Keywords: Emotion tracking, creative drawing, self-expression, visual metaphors, personal informatics, emotion recognition, mental well-being, HCI, digital tools, self-reflection.

Background and Problem

• Problem / challenge: Existing emotion-tracking methods often rely on predefined templates or automated sensing, which fail to capture the deeply personal, context-dependent nature of emotional expression through visual art. Prior research has not adequately explored how people use drawing as an everyday emotion-tracking practice or how such practices can be generalized across individuals.
• Significance: Understanding how individuals express emotions through drawing can inform the design of tools that promote emotional well-being, self-reflection, and creativity, offering an alternative to rigid or impersonal emotion-tracking systems.
• Motivation and related work: Previous studies have explored relationships between emotions and visual creations, often focusing on predefined emotional categories or therapist-facilitated interpretations. However, the nuanced and personal meaning-making processes behind everyday emotion drawings remain underexplored. This study builds on dimensional and constructionist theories of emotion to investigate drawing as a free, expressive modality for emotion tracking.

Solution

• Proposed approach: EmoFlow, an iOS application enabling digital drawing for emotion tracking, combined with lightweight data entry and reflective interviews.
• Novelty:
  1. Empirical evidence challenging universal mappings between visual features and emotions, emphasizing personal and context-driven meaning-making.
  2. Identification of four expression patterns in emotion drawings: emotion source, metaphorical representation, direct expression, and random drawing.
  3. Design implications for emotion-tracking technologies that prioritize creativity, interpretive openness, and user agency.
• Procedure and key techniques:
  1. A 14-day diary study with 21 participants using EmoFlow to create daily emotion drawings and record valence, arousal, and textual descriptions.
  2. Post-study debriefing interviews to explore participants’ reflections on their drawings and the emotion-tracking process.
  3. Multi-modal analysis of 252 drawings, including coding for expression patterns, personal styles, and statistical correlations between emotions and drawing behaviors.

Results

• Concrete findings:
  • Moderate positive correlations were found between emotional valence and both stroke count (r = 0.32) and drawing duration (r = 0.35). No significant correlations were observed between emotions and stylus pressure or expression patterns.
  • Four expression patterns were identified: emotion source (e.g., events, objects), metaphorical representation (e.g., weather, symbols), direct expression (e.g., emojis, text), and random drawing (e.g., abstract marks).
  • Participants maintained consistent personal styles (e.g., monochrome sketches, colorful geometrics) but occasionally shifted styles during atypical emotional states.
• Advantage over baselines: Unlike prior tools with predefined templates, EmoFlow allowed free-form, creative expression, revealing the deeply personal and dynamic nature of emotion drawing. This approach highlighted the limitations of universal mappings between visual features and emotions.
• Experiments / evaluation:
  • Participants: 21 university students (15 female, 6 male, aged 18–31).
  • Data: 252 drawings, emotion ratings (valence, arousal), textual descriptions, and interview transcripts.
  • Metrics: Drawing behaviors (e.g., stroke count, duration, stylus pressure), expression patterns, and qualitative themes from interviews.
• Limitations and future work:
  • Limited cultural diversity (predominantly Asian participants) and small sample size.
  • Variability in artistic expertise among participants.
  • Future work should explore cross-cultural differences, neurodiverse populations, and multimodal emotion-tracking approaches (e.g., combining drawing with speech or text).

Summary

This study introduced EmoFlow, a digital tool for emotion tracking through creative drawing, and conducted a 14-day diary study with 21 participants. The analysis identified four expression patterns (emotion source, metaphorical representation, direct expression, and random drawing) and highlighted the deeply personal and context-dependent nature of emotion drawings. While no universal correlations were found between emotions and visual features, the study revealed that drawing fosters intuitive, playful, and safe emotional expression. These findings underscore the need for emotion-tracking technologies that prioritize creativity, interpretive openness, and user agency, setting a new agenda for expression-centered design in HCI and mental well-being.