This deep learning framework for yoga asana identification is designed to automate the recognition and classification of yoga poses using computer vision and artificial intelligence. The system operates in several key stages:

1. Dataset Collection

The process begins with the acquisition of a dataset containing images or videos of individuals performing various yoga asanas. The dataset is diverse, capturing different body types, angles, lighting conditions, and environments to ensure robust model performance.

2. Preprocessing

Each image is preprocessed to improve its quality and consistency. This stage may involve resizing, normalization, noise reduction, background subtraction, and pose alignment. The goal is to standardize input data for optimal learning.

3. Transfer Learning for Feature Extraction

Preprocessed images are passed through a pre-trained deep learning model (e.g., VGG16, ResNet, InceptionNet) using transfer learning. These models, trained on large image datasets like ImageNet, act as efficient feature extractors, capturing high-level visual patterns relevant to human posture and pose.

4. Feature Set Generation

The output of the transfer learning model is a feature set, representing abstracted information from each image (e.g., joint positions, angles, spatial relationships). This set forms the input for the final classification model.

5. Training and Testing

The feature set is divided into training and testing datasets. The training set is used to teach the Deep Neural Network (DNN) classifier to associate feature patterns with specific yoga poses, while the testing set is used to evaluate model accuracy and generalization.

6. DNN Classifier

The core of the system is a DNN classifier, which consists of multiple hidden layers with interconnected neurons. It learns non-linear mappings between feature representations and yoga asana labels (e.g., Tadasana, Vrikshasana, Bhujangasana). The network uses backpropagation and gradient descent to minimize classification errors.

7. Accuracy Evaluation

After each training epoch, the model's accuracy is evaluated. If the model fails to reach the desired accuracy threshold, it undergoes further training iterations. This loop continues until the system achieves satisfactory classification performance.

8. Model Checkpointing

Once the required accuracy is attained, the trained model is saved as a checkpoint. This allows for reuse without retraining and can be deployed in real-time applications such as yoga learning apps, posture correction tools, or virtual yoga instructors.

Applications

• Real-time yoga posture correction
• Automated feedback systems in yoga apps
• Yoga pose detection in video conferencing
• Rehabilitation and therapy monitoring
• Fitness and wellness analytics platforms