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Leveraging data from wearables like Fitbit and other real-time sources, Dtwin empowers users to take control of their health with AI-driven guidance—not generic advice.\n\n---\n\n## 🚀 Live Demo\n\n🔗 **Live Web App**: [https://dtwin-health.app](https://dtwin.netlify.app/)\n\n**NOTE:** This website is only mobile compatible.\n\n🔗 **Backend API**: [https://api.dtwin-health.app](https://api.dtwin-health.app)\n\n🔗 **DTwin APK**: [Download APK](https://drive.google.com/file/d/1lcvgrF0WuAXMbseI71wi1fbvrDyDSP5v/view?usp=sharing)\n\n---\n\n## 📸 App Screenshots\n\n\n| 🫀 Heart Analytics | 🤖 Personalized Diet | 🔎 Gut Analysis |   |\n|:---------------:|:----------------:|:----------------------:|:-:|\n| ![](./public/screenshots/5.jpg) | ![](./public/screenshots/10.jpg) | ![](./public/screenshots/6.jpg) |\n\n| 📊 Main Dashboard | 🩺 Mental Wellness Chatbot | 📈 Variable Data |   |\n|:---------------:|:----------------:|:----------------------:|:-:|\n| ![](./public/screenshots/9.jpg) | ![](./public/screenshots/3.jpg) | ![](./public/screenshots/7.jpg) |   |\n\n---\n\n## 🧬 Features\n\n- 🩺 **Disease Prediction**  \n  Real-time risk prediction for:\n  - Heart Disease\n  - Diabetes\n  - Gut Health Disorders\n  - More conditions coming soon\n\n- 📊 **Real-Time Health Insights**  \n  Get instant health feedback based on:\n  - Fitbit data\n  - Heart rate variability\n  - Step count, sleep patterns, calorie burn\n\n- 🍽️ **Personalized Meal Plans**  \n  Custom diet suggestions tailored to each user's:\n  - Blood sugar trends\n  - Activity level\n  - Gut microbiome profiles\n\n- 🏃‍♂️ **Custom Exercise Recommendations**  \n  Dynamic fitness plans based on:\n  - Energy expenditure\n  - Weight trends\n  - Recovery and fatigue scores\n\n- 📈 **Smart Analytics Dashboard**  \n  Visualizations and predictions to help you make smarter decisions about your health.\n\n---\n\n## 🛠️ Tech Stack\n\n### 🔷 Frontend\n- React.js\n- TailwindCSS\n- Chart.js \u0026 D3.js (for visualizations)\n\n### 🔶 Backend\n- Node.js\n- Express.js\n- REST API (with token-based authentication)\n- MongoDB (for user data)\n\n### 🧪 Machine Learning\n- Python (Scikit-learn, TensorFlow)\n- Trained on real-time + public datasets\n- Served via FastAPI/Python microservices\n\n### 📱 Mobile App\n- Capacitor.js\n- Android \u0026 iOS builds\n\n---\n\n## 📂 Datasets Used\n\n- **Heart Disease Prediction**: Garmin Real-Time Sensor Dataset  \n- **Diabetes Prediction**: IICMBC Clinical Dataset  \n- **Gut Health**: American Gut Project (AGP)\n\nAll models are trained with preprocessing pipelines and continuously updated using anonymized wearable data.\n\n---\n\n## ⚙️ Installation (Development)\n\n### Prerequisites\n- Node.js \u0026 npm\n- Python 3.9+\n- MongoDB running locally or Atlas DB\n\n### Clone and Run\n\n```bash\ngit clone https://github.com/yourusername/dtwin.git\ncd dtwin\n\n# Install frontend\ncd client\nnpm install\nnpm start\n\n# Install backend\ncd ../server\nnpm install\nnpm run dev\n\n# Run ML services\ncd ../ml-service\npip install -r requirements.txt\nuvicorn app:main --reload\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprasanthyt%2Feb-dtwin","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprasanthyt%2Feb-dtwin","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprasanthyt%2Feb-dtwin/lists"}