Classification of Surya Namaskar Yoga Asana

Abstract:

Surya Namaskar, or Sun Salutation, is a foundational sequence in yoga practice consisting of postures that enhance physical and mental well-being. Accurate classification of these postures is critical for fitness tracking and self-correction in remote yoga sessions. This project presents an AI-based system that uses Convolutional Neural Networks (CNN) to classify images of users performing Surya Namaskar poses. The model is deployed using Flask for real-time prediction, with MySQL used for storing user session logs and feedback. The system enhances yoga practice by providing posture verification and real-time feedback, promoting accuracy and safety.

Introduction:

The increasing interest in digital health and fitness has spurred the development of intelligent systems that guide and monitor physical activities. Among various disciplines, yoga has emerged as a therapeutic and preventive health practice. However, incorrect posture execution can lead to health issues. To address this, the project proposes an AI-powered system for automatic classification of Surya Namaskar yoga poses, enabling users to practice yoga effectively from remote locations while receiving real-time corrective feedback.

Problem Statement:

Manual supervision during yoga practice is not always feasible, especially in online or self-guided sessions. Users may perform Surya Namaskar incorrectly, leading to ineffective practice or potential injury. There is a need for an automated system that can classify each pose in real-time and provide corrective insights to the practitioner.

Existing System and Disadvantages:

Existing System:

• Traditional yoga apps or video tutorials that provide only visual guidance.
• Some systems use basic motion tracking sensors for pose detection.

Disadvantages:

• No real-time feedback mechanism.
• Lack of personalized correction.
• Sensor-based systems are costly and impractical for mass adoption.
• No deep learning-based image classification for specific sequences like Surya Namaskar.

Proposed System and Advantages:

Proposed System:

• A deep learning model (CNN) trained to classify images of Surya Namaskar postures.
• Flask-based web interface for uploading or capturing pose images.
• MySQL database to manage user data, history, and feedback.

Advantages:

• Real-time pose classification with high accuracy.
• Cost-effective as it uses a webcam or smartphone camera.
• Easy-to-use interface for self-guided yoga.
• Helps in maintaining correct posture and progression tracking.

Modules:

1. User Interface Module:
   • Pose image upload input.
   • Result display with corrective suggestions.
2. Preprocessing Module:
   • Image resizing, normalization, and augmentation.
3. CNN Classification Module:
   • Deep learning model to classify the  Surya Namaskar poses.
4. Database Management Module:
   • Stores user history, classification results, and feedback.
5. Admin Panel (Optional):
   • Monitor user activity, dataset updates, and system logs.

Algorithms:

Convolutional Neural Network (CNN)

Training Techniques:

• Data Augmentation (Rotation, Flip, Zoom)
• Early Stopping
• Adam Optimizer

Software Requirements:

• Programming Language: Python
• Framework: Flask
• Deep Learning Library: TensorFlow/Keras or PyTorch
• Database: MySQL
• Others: OpenCV, NumPy, Matplotlib, scikit-learn

Hardware Requirements:

• Processor: Intel i5 or above
• RAM: Minimum 8 GB
• GPU: Recommended (NVIDIA GTX 1050 or better for training)
• Camera: HD Webcam for real-time pose input

Conclusion:

The proposed system successfully classifies Surya Namaskar yoga asanas using CNN, providing users with intelligent feedback and guidance. It bridges the gap between traditional yoga practice and modern AI technology, empowering users to perform yoga with precision even in remote or solo settings.

Future Enhancement:

• Integrate voice-based feedback using NLP.
• Extend classification to other yoga sequences beyond Surya Namaskar.
• Incorporate real-time video analysis and continuous tracking.
• Develop a mobile application for on-the-go use.
• Add posture correction suggestions using skeleton pose estimation (e.g., OpenPose or Mediapipe).