Smart-Fit: Intelligent Exercise Tracking System

Smart-Fit is an advanced exercise tracking and analysis system that uses machine learning to monitor and analyze barbell exercises. The system processes motion data to track repetitions, detect exercise patterns, and provide insights into workout performance.

Table of Contents

• Features
• Installation
• Usage
• Project Structure
• Technologies Used
• Contributing
• License

Features

• Exercise Repetition Counting: Automatically counts repetitions during barbell exercises
• Motion Data Analysis: Processes and analyzes motion data to identify exercise patterns
• Outlier Detection: Identifies and removes anomalous data points for accurate analysis
• Temporal and Frequency Analysis: Provides both time-domain and frequency-domain analysis of exercise movements
• Machine Learning Integration: Uses advanced algorithms to classify and predict exercise patterns
• Data Visualization: Includes tools for visualizing exercise data and analysis results

Installation

Prerequisites

• Python 3.8.15
• Conda package manager

Setup Instructions

1. Clone the repository:

git clone https://github.com/yourusername/smart-fit.git
cd smart-fit

2. Create and activate the conda environment:

conda env create -f environment.yml
conda activate tracking-barbell-exercises

3. Verify the installation:

python -c "import numpy; import pandas; import matplotlib"

Usage

The project is organized into several key components:

1. Data Processing:

   • Use src/features/remove_outliers.py to clean motion data
   • Apply src/features/count_repetitions.py to track exercise repetitions
   • Utilize src/features/build_features.py for feature engineering

2. Model Training:

   • Run src/models/train_model.py to train the exercise classification model
   • Use src/models/LearningAlgorithms.py for custom learning algorithms

3. Visualization:

   • Access visualization tools in the src/visualization directory

Project Structure

smart-fit/
├── data/                  # Raw and processed data
├── report/               # Analysis reports and visualizations
├── src/                  # Source code
│   ├── data/            # Data processing utilities
│   ├── features/        # Feature engineering modules
│   ├── models/          # Machine learning models
│   └── visualization/   # Data visualization tools
├── environment.yml      # Conda environment configuration
├── LICENSE              # Project license
└── README.md           # Project documentation

Technologies Used

• Python 3.8.15: Core programming language
• NumPy: Numerical computing
• Pandas: Data manipulation and analysis
• Matplotlib: Data visualization
• Jupyter: Interactive development environment
• Machine Learning: Custom learning algorithms for exercise classification

Contributing

We welcome contributions to Smart-Fit! Please follow these steps:

1. Fork the repository
2. Create a new branch for your feature
3. Make your changes
4. Submit a pull request

License

This project is licensed under the terms specified in the LICENSE file.

Note

This project is currently under active development. Some features may be incomplete or subject to change. We welcome feedback and contributions from the community.