Fitness Tracker Application

The Fitness Tracker Application is an advanced health and fitness monitoring system that utilizes machine learning and AI models to provide personalized health recommendations. This README provides an overview of the project, how to set it up, and how to use it.

Table of Contents

• Features
• Prerequisites
• Installation
• Usage
• Contributing
• License

Features

• User profile management for storing personal information.
• Simulated data collection from wearables (steps, sleep hours) for multiple users.
• Generation of personalized health recommendations based on user profiles.
• Potential for real data integration from fitness wearables and IoT devices.
• Extensible machine learning models for health recommendations.

Usage

1. User Profile Management:

Create user profiles by instantiating the UserProfile class. Update user profiles with personal information, such as name, age, gender, weight, and height.

2. Data Collection from Wearables:

Use the WearableDevice class to simulate data collection from wearables. Update user profiles in real-time with steps and sleep hours data.

3. Personalized Health Recommendations:

Employ the HealthRecommendation class to generate personalized health recommendations based on user data. Recommendations may include dietary advice, exercise plans, and sleep guidelines.

4. Advanced Integration:

For real-world use, integrate the application with actual fitness wearables and IoT devices to gather accurate data.

Prerequisites

• Python 3.6+
• Required Python libraries (NumPy, pandas, scikit-learn, etc.) - detailed in the project files.
• (Optional) Real fitness wearable devices for data integration.