Mood Pulse - Details

• Users often struggle to articulate or manage their emotional states, especially during stress, anxiety, or burnout.
• Existing mood trackers are static and lack real-time emotional support or actionable suggestions.
• Mental wellness apps fail to personalize experiences or adapt to the user’s emotional journey over time.

• Build a conversational AI-based journaling assistant that enables users to speak or type their thoughts.
• Use NLP to detect emotional tone (e.g., stress, sadness, joy) and recommend context-aware activities (e.g., breathing exercises, music, prompts).
• Implement mood tracking, visual insights, and habit suggestions based on ongoing interactions.

Methodologies Used:

• Secondary Research: Reviewed mental health platforms (e.g., Woebot, Moodnotes, Mind Journal).
• User Interviews: Conducted 10 interviews with individuals aged 18–35 experiencing emotional fatigue or anxiety.

Key Insights:

• Voice journaling felt more therapeutic than typing for users under emotional stress.
• Users appreciate personalized wellness suggestions but find generic notifications annoying.

• Prioritize a conversational interface that feels empathetic, calm, and non-intrusive.
• Design for low cognitive effort, especially during negative emotional states.
• Incorporate habit-forming UX patterns through small, meaningful suggestions (micro-rewards, reflection prompts).
• Use visual mood tracking to help users reflect on emotional patterns over time.

• Home (Mood selections, AI Interaction Screen)
• Mood Entry Prompt
• Mood History Tracker
• Habits & Suggestions
• Reflection Journal

• Typography: Clean, calming sans-serif fonts (Poppins, Lato)
• Color Palette: Soft gradients (e.g., calming greens, blues) with a dark mode
• Components: Floating mic button, Emotion icons, AI message bubbles
• Accessibility: Large tap targets, voice-to-text input, dyslexia-friendly contrast modes
• Micro-interactions: Subtle animations on mic input, mood detection feedback