FitnessApp

Overview

This backend system powers a fitness tracking application, leveraging AI to provide personalized fitness recommendations, track user activities, and monitor progress. It offers a robust API for managing users, logging workouts, setting fitness goals, and delivering AI-driven insights.

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Features

User Management

• User registration and login.
• Encrypted password storage.
• User profiles include age, weight, height, and fitness goals.

Activity Tracking

• Log fitness activities (e.g., running, cycling, weightlifting).
• Each activity includes:
  • Activity Type: Exercise type.
  • Duration: Time spent.
  • Calories Burned: Estimated energy expenditure.
  • Timestamp: Activity date and time.

Goal Setting

• Set and update fitness goals (e.g., weight loss, muscle gain, distance running).

AI-Driven Insights

• Personalized workout recommendations.
• Progress tracking toward fitness goals.
• Motivational feedback based on user activity.

Data Visualization

• Endpoints for retrieving activity data for visualization (e.g., charts of calories burned or workouts logged).

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Technical Stack

• Backend Frameworks:
  • Node.js (Express) for core APIs.
  • FastAPI (Python) for AI model inference.
• Database: MongoDB.
• Machine Learning Model: XGBoost for workout classification.

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Machine Learning Details

The AI model is built to classify workouts based on user activity data. Key details:

1. Dataset: fitness_tracker_dataset.csv
2. Preprocessing:
   • Features: Exclude gender, duration, calories_burned, intensity, workout_type, and calories_intake.
   • Target: workout_type.
   • Standardized using StandardScaler.
3. Model: XGBoost Classifier with hyperparameters:
   • n_estimators=100
   • max_depth=5
   • learning_rate=0.1
4. Accuracy: Achieved significant training and testing accuracy after model evaluation.

Refer to the main.py file for implementation details.

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Installation and Setup

Prerequisites

• Python 3.9+
• Node.js 14+
• MongoDB

Steps

1. Clone the repository.
2. Navigate to the backend folder:

   cd backend

3. Install Node.js dependencies:

   npm install

4. Navigate to the FastAPI service:

   cd fastapi-service

5. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

6. Install Python dependencies:

   pip install -r requirements.txt

7. Start the Node.js backend:

   npm start

8. Start the FastAPI service:

   uvicorn main:app --reload

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API Endpoints

Node.js API

• POST /users: Create a new user.
• GET /users/:id: Retrieve user profile.
• POST /activities: Log a fitness activity.
• GET /activities/:id: Retrieve activity logs.
• POST /goals: Set/update fitness goals.
• GET /insights/:id: Get AI-driven insights.

FastAPI Service

• POST /predict: Classify workout type using the AI model.

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Contributing

Contributions are welcome! Please ensure to follow standard practices when adding features or fixing issues.

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License

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

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Let me know if you'd like modifications!