Yoga Posture Classification using Deep Learning
Gokul Ananth, Radhakrishnan Anuradha · 2022
Human Posture detection using Deep learning techniques are widely useful in many areas like face recognition, video games, virtual reality, animations and many varieties of physical training. In this work, we are applying deep learning techniques like convolution neural networks (CNN) in the field of Yoga. It is a form of physical exercise which helps to improve both the physical and mental state of humans. There are different styles of yoga that combine physical poses, breathing techniques, and meditation or relaxation to promote mental and physical well-being. We have used a dataset which consists of five different yoga poses for training the model and evaluating the prediction accuracy of the model. The system recognizes a yoga pose from a given image using a CNN model and three CNN architectures such as VGG16, VGG19 and MobileNet. In comparison, among the four, VGG19 model achieved an accuracy of 99.49% which is higher than the other models.