Computerized Framework for Yoga Pose Estimation Using Deep Learning Algorithm

This research paper explores yoga design prediction using deep learning. A new method for automatic detection and classification of yoga poses with images or videos is presented, which includes the development of a neural network (CNN)-based deep learning model. The model uses spatial relationships and local patterns in yoga poses using convolutional techniques for feature extraction. In addition, layers are used to capture the moment of addiction in video-based yoga design projections. creates a large database of recorded images and videos of yoga poses for training and test models. This database contains many yoga practitioners of various skill levels. The main points and tags of the site are recorded in the data to monitor the learning of the deep learning model. To start the model, adaptive learning is performed using weights previously learned from a large dataset. This adaptive learning improves the performance of the model by using the information learned from the dataset. Experiments were conducted to evaluate the effectiveness and accuracy of the proposed model. The results demonstrate the deep learning model's ability to evaluate correct yoga poses from photos and videos. The performance model shows that it can provide real feedback, monitor progress, and help users practice yoga correctly. The results of this study lead to predictive yoga using the power of deep learning. The proposed model improved the general yoga practitioner, paving the way for personal yoga practice, virtual training, and non-invasive physical therapy.