Posture Detection Using Machine Learning in Python

Posture Detection & Pose Classification

This is a professional-grade, paid project developed using Python and Flask. The application detects human posture and classifies different body poses in real-time using machine learning and live video input. It’s especially useful for fitness monitoring, posture correction, and gesture-based control systems. Designed for both developers and researchers, the system offers accurate, real-time feedback, making it a powerful tool for body movement analysis.

Overview Table

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Project Description

This project provides a web interface that allows users to detect and classify body postures in real time using their webcam. It supports multiple predefined poses such as:

• Tree Pose
• T Pose
• Warrior II Pose

The application leverages the MediaPipe library to detect body landmarks and then classifies the user’s posture using trained machine learning models. This system is designed to help users improve posture awareness, especially in yoga, pilates, and other fitness routines.

Built with a user-friendly interface, this web app ensures a smooth experience for users, whether they’re tracking performance or integrating gesture-based controls into broader applications.

Technologies and Libraries Used

• Python – For backend processing and logic.
• Flask – Lightweight web framework used for serving the application.
• MediaPipe – Google’s ML solution for high-fidelity body landmark detection.
• OpenCV – Used for video stream handling and image processing.
• Matplotlib – Optional visualization and plotting support.

Available Features

• Real-time posture detection using a webcam
• Classification of yoga poses such as Tree Pose, T Pose, Warrior II
• Web-based user interface
• No external database integration, making it lightweight and simple
• Report and documentation files included for reference

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