Mood Map
Visual Emotion Tracking Without Words
Mood Map is a conceptual mobile app that helps people track and reflect on emotions without writing or journaling. I designed the project end-to-end, including research, wireframes, and high-fidelity prototypes using Figma.
The concept came from noticing how many mood tracking apps rely on writing, labels, or streaks. For people who feel overwhelmed or emotionally blocked, this can create pressure rather than clarity.
Goal: Create a mood tracking experience that feels visual, gentle, and reflective instead of clinical or task-driven.
Project Overview
Role
Solo junior UX/UI Designer
Responsible for research, wireframing, interaction design, and prototyping.
Tools
Figma
User Interviews
Competitive Analysis
Wireframing & Prototyping
Timeline
3–4 week concept project
Research, Design, Testing, and Iteration
Case Study
The Problem
People who want to understand their emotional patterns often struggle with text based journaling. Writing about feelings can feel time consuming or emotionally difficult, and many users experience multiple emotions at once. Some mood apps also rely heavily on streaks or progress tracking, which can feel judgmental, while others feel overly clinical rather than supportive.
Most mood tracking apps depend on labels or written entries, which can discourage consistent use.
Problem Statement
People who want to reflect on their emotional patterns lack mood tracking tools that feel low pressure and non verbal, leading to inconsistent use and limited self awareness.
Research
To better understand user behavior, I conducted informal interviews with people who had tried mood or journaling apps, reviewed user feedback on popular mood tracking platforms, and performed a competitive analysis of Daylio, Stoic, and other journaling tools.
Key insights
Writing was the biggest barrier to consistent mood tracking. Many users experienced multiple emotions at the same time and wanted tools that could reflect that complexity. Users were more interested in identifying patterns and context than receiving advice. Progress bars and streaks often created guilt rather than motivation, and privacy and emotional safety were major concerns.
Personas
Reflective Riley (26)
Emotionally aware but struggles to express feelings through writing. Riley wants reflection tools that feel calm and pressure free.
Busy Morgan (34)
Uses wellness apps occasionally and prefers quick emotional check ins and simple insights.
Ideation
Using How Might We questions and sketching, I explored several concepts including mood sliders, emoji systems, and visual emotion mapping. To keep the project focused, I narrowed the concept to visual emotion tracking that does not require text input.
Core features
Visual Mood Check In allows users to log emotions through color, shape, and intensity.
Emotion Blending supports mixed emotions rather than forcing users to select one label.
Mood Canvas creates an abstract visual history of emotional patterns.
Pattern Insights reveal emotional frequency and trends over time.
Observational Feedback presents insights neutrally to avoid judgment.
Primary Flow
Onboarding - Mood Check In - Visual Overview - Pattern Insights
Design Process
I began with grayscale wireframes to focus on layout and usability before introducing visual design elements.
Key decisions included using bottom navigation to separate Check In, Overview, and Insights, designing the mood check in process to take less than ten seconds, replacing written reflections with simple tap interactions, and removing progress focused language in favor of neutral observation.
The visual design uses soft gradients, abstract shapes, and subtle motion to make emotional data feel expressive rather than analytical.
Usability Testing
I tested the interactive prototype with six users using think aloud tasks such as logging a mood and reviewing emotional patterns.
Users appreciated the ability to log emotions without typing, and emotion blending felt more realistic than selecting a single mood. Pattern insights were most effective when presented neutrally, and subtle animations helped the experience feel engaging without becoming distracting.
Final Design Highlights
Visual Mood Check In allows users to log emotions through color, shape, and intensity instead of text.
Emotion Patterns track emotional frequency and range over time.
Time of Day Rhythm reveals emotional trends throughout the day.
Mood Motion uses subtle animation to reflect emotional tone.
Creative Mode allows users to export moods as abstract artwork without labels.
Outcomes
User feedback suggested the experience felt calm, approachable, and easier to engage with than traditional journaling apps. Several testers noted that the app felt less intimidating than text-based mood tracking tools.
Reflection
This project highlighted how reducing friction can encourage more honest emotional input. Language and tone strongly influence emotional safety, and visual abstraction can make sensitive information feel more personal. Careful scoping also helped maintain clarity and usability throughout the design process.
Future improvements could include home screen widgets for faster mood check ins and expanded accessibility options for motion and color sensitivity.