Real-Time Ai Fitness Counter With Python & Computer Vision

Smart Fitness: Real-Time Exercise Counting with AI using python and Computer Vision

What you'll learn

Understand the fundamentals of AI-based exercise tracking and its significance in real-time fitness monitoring.

Set up a Python development environment using Tkinter for UI and MediaPipe for pose estimation.

Implement real-time exercise counting for squats, push-ups, chest flys, and dumbbell lifts using MediaPipe.

Process live video feeds or uploaded videos to count exercises and provide feedback to users.

Learn pose detection techniques and how to apply them to analyze human motion accurately.

Develop a user-friendly interface with Tkinter to visualize exercise counts and provide real-time updates.

Optimize the system for accuracy and real-time performance in tracking and counting exercises.

Tackle challenges such as occlusions, variations in body posture, and different camera angles.

Explore potential applications in fitness training, rehabilitation, and personal workout tracking.

Requirements

Basic understanding of Python programming (recommended but not mandatory).

A laptop or desktop computer with internet access (Windows OS with a minimum of 4GB RAM).

No prior knowledge of AI or Machine Learning is required—this project is beginner-friendly.

Enthusiasm to learn and build practical AI-driven fitness applications.

Description

Welcome to the Smart Fitness: Real-Time Exercise Counting with AI and Computer Vision course!In this hands-on project, you’ll learn how to build an AI-powered system that accurately counts exercises like squats, push-ups, chest flys, and dumbbell lifts using MediaPipe for pose estimation and Tkinter for real-time UI updates.This project leverages MediaPipe’s advanced pose detection models to track body movements and count exercises performed in front of a camera or from an uploaded video. You’ll gain practical experience in:• Setting up Python with Tkinter for a graphical user interface.• Using MediaPipe’s Pose Estimation to analyze human movements.• Implementing real-time exercise counting algorithms for different workouts.• Processing video streams to count repetitions from live or uploaded videos.• Displaying results dynamically in a Tkinter-based UI.• Handling challenges like occlusions, camera angles, and motion variations.By the end of this course, you will have built a fully functional AI-powered fitness tracking system, perfect for personal workouts, fitness coaching, and rehabilitation monitoring. You’ll also understand how to fine-tune your system for different body types, movement speeds, and exercise routines.Join us and start building your Smart Fitness AI Assistant today to enhance performance and achieve smarter fitness goals with the power of AI!

Overview

Section 1: Introduction of the Human Fitness Tracking System

Lecture 1 Course Introduction and Features

Section 2: Environment Setup for Python Development

Lecture 2 Installing Python

Lecture 3 VS Code Setup for Python Development

Section 3: Project Overview & Purpose

Lecture 4 Human Fitness Tracking System Project Overview

Section 4: Packages Overview & MediaPipe Initialization

Lecture 5 Packages Overview & MediaPipe Initialization

Section 5: Calculating Angles in Pose Estimation

Lecture 6 Calculating Angles in Pose Estimation

Section 6: Logic Behind Repetition Counting

Lecture 7 Logic Behind Repetition Counting

Section 7: Tkinter Log Window & Variable Initialization

Lecture 8 Tkinter Log Window & Variable Initialization

Section 8: Model Inference and Code Explanation

Lecture 9 Model Inference and Code Explanation

Section 9: Tkinter Implementation for UI

Lecture 10 Tkinter Implementation for UI

Section 10: Package Installation Guide

Lecture 11 Package Installation Guide

Section 11: Code Execution Workflow

Lecture 12 Code Execution Workflow

Section 12: Wrapping Up

Lecture 13 Course Wrap-Up

Students and beginners interested in AI-based fitness applications.,Fitness trainers and enthusiasts looking to integrate AI into workout tracking.,Developers aiming to explore computer vision applications in fitness and health tech.,Researchers interested in pose estimation and motion tracking for human activities.