Our Instructors Collaborate With Top Tech Leaders
Startups
Microsoft
NVIDIA
Build an AI-based fitness assistant that counts push-ups,
squats, and other exercises using pose detection with OpenCV and
Mediapipe. Get real-time feedback.
Beginner to Intermediate. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to build AI-powered accident detection systems and secure computer vision jobs or freelance projects.
Learn how AI tracks human body movements to monitor fitness activities and exercise form.
Use Python with OpenCV and pose estimation tools like MediaPipe or OpenPose for accurate body tracking.
Build a real-time system that detects workout types, counts reps, and checks posture during exercises.
Store exercise data such as reps, sets, and timestamps into a database for fitness tracking.
Create an interactive interface to show live video, posture feedback, and rep counts.
Finish the course and receive a verified certificate of success
Muhammad Yaqoob is the founder of Tentosoft Pvt Ltd and a seasoned Computer Vision expert. With 10+ years of experience and over 5,000+ students taught globally, he brings deep industry knowledge and a passion for practical, hands-on learning.
Gain practical experience by building industry-relevant computer vision applications from scratch.
Discover the fundamentals of building an AI-powered fitness counter that tracks workouts in real-time using computer vision and pose estimation techniques.
Course Introduction and Features
Uses sensors or video to track movements like exercise or posture
Employs AI models to analyze body pose and motion in real-time
Supports personalized fitness goals and real-time corrections
Set up your Python development environment with essential tools and libraries like OpenCV and Mediapipe to enable real-time workout tracking.
Installing Python
VS Code Setup for Python Development
Install Python from the official Python.org website
Optionally install Docker for containerized development
Understand the goals and structure of the AI fitness counter project, focusing on its purpose to provide accurate real-time workout tracking using computer vision.
Human Fitness Tracking System Project Overview
Defines the core goals and functionality of the project
Provides context for documentation and future scalability
Explore key Python packages like OpenCV, NumPy, and Mediapipe, and learn how to initialize them for real-time pose detection in fitness tracking applications.
Packages Overview & Mediapipe Initialization
Lists essential Python packages like OpenCV, NumPy, and Mediapipe
Enables real-time video frame processing for fitness tracking
Prepares the system for accurate and responsive detection
Learn to calculate joint angles using Mediapipe's landmarks to enable precise tracking of workout movements in real-time.
Calculating Angles in Pose Estimation
Uses landmarks from Mediapipe to track body joints
Visualizes angle values over video frames for feedback
Forms the core logic of motion-based activity recognition systems
Understand the logic for counting exercise repetitions by analyzing joint angle thresholds in real-time for accurate workout tracking.
Logic Behind Repetition Counting
Uses joint angle thresholds to detect movement phases
Useful in fitness apps for accurate exercise tracking
Build a Tkinter-based interface to display real-time workout data, initializing variables to track exercise states and counts effectively.
Tkinter Log Window & Variable Initialization
Initializes GUI variables to track exercise state and counts
Iterating designs based on user insights
Dive into model inference to process real-time video data for workout tracking, with detailed code explanations for the AI fitness counter system.
Model Inference and Code Explanation
Loads the trained model to make real-time predictions
Demonstrates practical usage of AI models in fitness applications
Critical for understanding how the system operates during live use
Implement a user-friendly Tkinter interface to visualize real-time workout tracking results, integrating AI model outputs for enhanced usability.
Tkinter Implementation for UI
Creates a user-friendly interface using the Tkinter library
Integrates the detection model output into the GUI window
Facilitates end-to-end usability for the fitness tracking system
Follow a step-by-step guide to install essential libraries like OpenCV, NumPy, and Mediapipe, setting the foundation for your AI fitness counter.
Package Installation Guide
Installs essential libraries like OpenCV, NumPy, and Mediapipe
Acts as the foundational step before coding or testing begins
Is This Course Right for You?
Kickstart your AI journey with structured, hands-on learning.
Build a portfolio that recruiters can't ignore.
Add powerful AI/CV features to your apps and software.
Upskill for higher-paying, future-ready tech roles.
Build Smarter, more intelligent applications.
Transition into AI even with zero background.
One-time payment for lifetime access to all course materials and updates
Get hands-on experience with real-world projects designed to sharpen your technical skills and build your confidence. Each project is crafted to help you apply concepts practically, write cleaner code, and prepare for real developer challenges.
Highly recommended for small teams who seek to upgrade their time & perform.
₹ 6720 inclusive of GST ₹ 13999
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