SkinSync - Your Pocket Dermatologist

SkinSync is a full-stack mobile application that provides AI-powered skin analysis, personalized skincare recommendations, and progress tracking. This project includes both the React Native frontend and Node.js/Express backend.

Features

• AI Skin Analysis: Upload photos of your face for automated skin condition detection
• Personalized Recommendations: Get tailored skincare advice based on your unique skin profile
• Progress Tracking: Monitor improvements in your skin health over time
• Daily Routines: Manage morning and evening skincare routines
• Community Support: Connect with others on similar skincare journeys
• GlowCoins Rewards: Earn points for consistent usage and unlock premium features

Technology Stack

Frontend

• React Native / Expo
• React Navigation v7
• Expo Router v5
• Lucide Icons
• Expo Camera for image capture
• React Native Reanimated for animations

Backend

• Node.js
• Express.js
• MongoDB / Mongoose (architecture ready for DB connection)
• RESTful API design

Machine Learning / AI Components

• Simulated skin analysis pipeline
• Image processing for feature extraction
• Skin type classification
• Condition detection algorithms
• Recommendation engine based on analysis results

Project Structure

skinsync/
├── app/                   # Main Expo app directory with Expo Router
│   ├── _layout.tsx        # Root layout
│   ├── (tabs)/            # Tab-based navigation
│   │   ├── _layout.tsx    # Tab configuration
│   │   ├── index.tsx      # Home/Dashboard screen
│   │   ├── analysis.tsx   # Analysis screen
│   │   ├── routines.tsx   # Routines screen
│   │   └── profile.tsx    # User profile screen
│   └── analysis/          # Analysis flow screens
│       ├── camera.tsx     # Photo capture screen
│       └── results.tsx    # Analysis results screen
├── components/            # Reusable React components
├── constants/             # App constants and theme
├── hooks/                 # Custom hooks
├── services/              # Frontend services for API calls
├── assets/                # Images, fonts, etc.
├── server/                # Backend Node.js/Express application
│   ├── index.js           # Server entry point
│   ├── routes/            # API route handlers
│   ├── models/            # MongoDB models
│   └── services/          # Backend services including ML processing
└── package.json           # Project dependencies

Machine Learning Architecture

The AI skin analysis pipeline consists of the following components:

1. Image Pre-processing

   • Image normalization and standardization
   • Face detection and extraction
   • Region of interest segmentation

2. Feature Extraction

   • Texture analysis for skin conditions
   • Color distribution analysis for redness, pigmentation
   • Pattern recognition for acne, dryness, etc.

3. Classification Models

   • Skin type classifier (Normal, Dry, Oily, Combination, Sensitive)
   • Condition detectors (Acne, Dryness, UV Damage, etc.)
   • Severity assessment (Low, Medium, High)

4. Recommendation Engine

   • Rule-based system using classification results
   • Personalized suggestions based on skin profile
   • Progress-aware recommendations that adapt over time

Installation and Setup

Prerequisites

• Node.js (v16 or higher)
• npm or yarn
• MongoDB (for full functionality)
• Expo CLI

Frontend Setup

1. Clone the repository:

   git clone https://github.com/yourusername/skinsync.git
   cd skinsync
   

2. Install dependencies:

   npm install
   

3. Start the Expo development server:

   npm run dev
   

Backend Setup

1. Navigate to the server directory:

   cd server
   

2. Install server dependencies (if not already installed):

   npm install
   

3. Start the server:

   npm run server
   

4. To run both frontend and backend simultaneously:

   npm run dev:all
   

Future Enhancements

• Real ML model integration with TensorFlow.js
• Cloud storage for user images and analysis results
• Authentication with JWT and social login
• Push notifications for skincare reminders
• Expanded product database with recommendations
• Dermatologist consultation booking feature
• Weather and location-based skincare adjustments

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Skin condition images used for training from open medical datasets
• Skincare product information from publicly available sources
• UI/UX design inspired by modern healthcare applications