| 1 -Calculating Angles in Pose Estimation.mp4 | 20.5 MB | ||
| 1 -Code Execution Workflow.mp4 | 97 MB | ||
| 1 -Course Introduction and Features.mp4 | 31.2 MB | ||
| 1 -Course Wrap-Up.mp4 | 9.1 MB | ||
| 1 -Human Fitness Tracking System Project Overview.mp4 | 22.7 MB | ||
| 1 -Installing Python.mp4 | 15.2 MB | ||
| 1 -Logic Behind Repetition Counting.mp4 | 127.8 MB | ||
| 1 -Model Inference and Code Explanation.mp4 | 116.6 MB | ||
| 1 -Package Installation Guide.mp4 | 24.8 MB | ||
| 1 -Packages Overview & MediaPipe Initialization.mp4 | 30.2 MB | ||
| 1 -Tkinter Implementation for UI.mp4 | 46.2 MB | ||
| 1 -Tkinter Log Window & Variable Initialization.mp4 | 30.1 MB | ||
| 2 -VS Code Setup for Python Development.mp4 | 19.2 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Pushups.mp4 | 4 MB | ||
| chest fly machine.mp4 | 2.4 MB | ||
| dumbbell workout.mp4 | 12.4 MB | ||
| fitness_tracking_final.py | 16.4 KB | ||
| requirements.txt | 0 B | ||
| squat.mp4 | 7.6 MB | ||
| ▲ 21 total files | |||
Real-Time AI Fitness Counter with Python & Computer Vision
https://WebToolTip.com
Published 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 46m | Size: 616 MB
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.
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