Yoga Pose Detection using Deep Learning | Python Final Year IEEE Project

Yoga Pose Detection using Deep Learning | Python Final Year IEEE Project 2024 - 2025.
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📌Project Title: Yoga Pose Detection using Deep Learning.
💡Implementation: Python.
🔬Algorithm / Model Used: VGG16 Model and SVC (Support Vector Classifier).
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.5000/

📘Project Abstract:
Yoga is a popular exercise that enhances both physical and mental well-being through various postures. Advances in artificial intelligence (AI) and machine learning (ML) have created the opportunity to develop systems that can automatically detect and classify yoga poses from visual input. This project, "Yoga Pose Detection using Deep Learning" aims to accurately identify yoga poses through image and webcam inputs, leveraging advanced machine learning techniques and deep learning methodologies. Leveraging Python for backend development, HTML, CSS, and JavaScript for frontend design, and Flask as the web framework, the application seamlessly integrates deep learning models for robust pose detection.
Two distinct approaches are employed: the VGG16 convolutional neural network model for static image-based detection, and the Support Vector Classifier (SVC) within the Mediapipe framework for real-time webcam-based detection. The VGG16 model, trained on a dataset of 1,081 images and tested on 470 images, demonstrates impressive accuracy with a training score of 98% and a testing score of 96%. For dynamic detection using a webcam, the SVC model achieves a training accuracy of 92.3% and a testing accuracy of 81.2%.

📌 IEEE Base Paper Title: Yoga Pose Recognition using Deep Learning.

📌REFERENCE:
Prachi Kulkarni, Shailesh Gawai, Siddhi Bhabad, Abhilasha Patil, Shraddha Choudhari, “Yoga Pose Recognition using Deep Learning”, 2024 International Conference on Emerging Smart Computing and Informatics (ESCI), IEEE CONFERENCE, 2024.

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