Project Details
Luna AI Health Companion App
Luna is a SwiftUI health companion app concept designed to support cycle tracking, symptom logging, medical history, and AI-assisted health conversations through a personalized mobile experience.
Overview
What the project does
Luna was designed as a mobile health companion app that helps users track menstrual cycle information, symptoms, severity levels, medical history, and personal health patterns over time.
The app concept combines structured health logging with an AI chatbot experience. The goal is for the chatbot to respond with awareness of the user's saved profile, symptom history, cycle details, and medical context instead of giving generic responses.
This project was focused on building a thoughtful app structure, planning user data flow, and exploring how AI can be connected to personal logs in a responsible and user-friendly way.
Product Concept
How the app is structured
Luna is planned around a few core sections: user profile, medical history, symptom tracking, cycle tracking, and AI chat support. Each section contributes context that can help the app provide more personalized responses and a clearer view of the user's health patterns.
The app was designed to feel simple and approachable while still collecting useful information. Instead of only asking users to enter general notes, the project explored more structured input fields such as cycle start date, cycle length, symptom type, symptom severity, and whether a symptom occurs only during the first few days or throughout the period.
Technical Flow
How the app workflow works
User Profile
The app collects basic user information needed to personalize the experience, such as age or birthday and cycle-related details.
Medical History
Users can enter health background information so the app can understand context before generating AI-supported responses.
Symptom Tracking
Symptoms can be logged individually with severity levels, timing, and additional notes to make the data more structured and useful.
Cycle Awareness
The app uses cycle-related inputs to better understand whether the user may be near the start of a cycle, currently menstruating, or tracking longer-term patterns.
AI Chat Context
The chatbot concept is designed to use saved health logs and profile data as context so responses are more relevant to the user's situation.
Safety-Oriented Responses
The app concept includes logic for stronger guidance when symptom severity is high, such as suggesting that the user consider medical advice for concerning symptoms.
Technical Work
What I built and planned
I worked on the SwiftUI app structure, screen planning, data models, and user input flow. The project included planning views for medical history, symptom logging, user profile data, and AI chat integration.
I also explored how local JSON storage could be used to save user profile information and health logs during early development. This helped create a foundation for passing user-specific context into future chatbot responses.
A major part of the project was thinking through how the chatbot should receive relevant health information. Instead of making the AI chat separate from the app, the plan was to connect it to the user's saved logs so the assistant could reference symptoms, cycle timing, and medical history in a more meaningful way.
AI Integration
How the chatbot was planned
The AI chatbot was planned as a support feature that could answer user questions while using structured app data as context. For example, if a user logged cramps, fatigue, or mood changes, the chatbot could respond with awareness of those entries instead of treating each question as isolated.
The chatbot concept was not meant to replace medical professionals. The goal was to provide supportive explanations, help users understand patterns, and guide users toward professional care when symptoms seem severe or concerning.
The project explored how OpenAI API integration could be connected to app data while keeping the user experience simple, conversational, and easy to understand.
Health Data Design
How user data was structured
The app was planned around structured health entries instead of only free-text notes. This makes the data easier to reuse across the app because each symptom can have its own severity, timing, and description.
This kind of structure would make it easier to show patterns over time, summarize recent logs, and provide the chatbot with cleaner context. It also supports future features such as trend views, recurring symptom detection, and cycle-based reminders.
Privacy + Safety
Important design considerations
Since Luna deals with personal health information, privacy and safety are important parts of the app design. A production version would need secure storage, clear consent, careful handling of chatbot context, and limits on what the AI can claim.
The app concept also includes safety-aware logic. For example, if a user enters very high symptom severity, the app can encourage them to seek medical advice rather than only providing general wellness suggestions.
My Contributions
What I worked on
SwiftUI Screens
Designed and planned SwiftUI screens for user profile, medical history, symptom tracking, cycle tracking, and AI chat support.
Data Models
Planned data structures for user profiles, medical history entries, cycle details, and symptom logs.
Symptom Input Flow
Worked on a more structured symptom input system where symptoms could be logged with severity, timing, and additional notes.
AI Chat Planning
Planned how the chatbot could use saved user logs and medical history as context for more personalized responses.
Local Storage
Explored JSON-based storage for saving user information and logs during early app development.
Safety Logic
Considered severity-based guidance so the app could respond more carefully when users log intense or concerning symptoms.
Tech Stack
Tools used
Status
SwiftUI prototype and AI integration planning.
Luna is currently a mobile app concept and prototype focused on health logging, AI-supported conversations, and structured user context. The project helped me practice SwiftUI app design, data modeling, local storage planning, and responsible AI feature design.