Yoga Pose Rating using Pose Estimation and Cosine Similarity

Abstract

One type of exercise that many people do today is yoga. However, doing yoga yourself without an instructor carries a risk of injury if not done correctly. This research proposes an application in the form of a website that can assess the accuracy of a person's yoga position, by using ResNet for pose estimation and cosine similarity for calculating the similarity of positions. The application will recognize a person's body pose and then compare it with the poses of professionals so that the accuracy of their position can be assessed. There are three types of datasets used, the first is the COCO dataset to train a pose estimation model so that it can recognize someone's pose, the second is a reference dataset that contains yoga poses performed by professionals, and the third is a dataset that contains pictures of yoga poses that are considered correct. There are 9 yoga poses used, namely Child's Pose, Swimmers, Downdog, Chair Pose, Crescent Lunge, Planks, Side Plank, Low Cobra, Namaste. The optimal pose estimation model has a precision value of 87% and a recall of 88.2%. The model was obtained using the Adam optimizer, 30 epochs, and a learning rate of 0.0001.