iYogacare: Real-Time Yoga Recognition and Self-Correction for Smart Healthcare
Abhishek Sharma, Yash Shah, Yash Agrawal, Prateek Jain
Abstract
Nowadays, yoga has become a part of life for many people. Exercises and sports technological assistances are implemented in yoga pose identification. In this work, a self-assistance-based yoga posture identification technique is developed, which helps users to perform yoga with the correction feature in real-time. The work also presents yoga-hand mudra (hand gestures) identification. The YOGI dataset has been developed, which includes ten yoga postures with around 400–900 images of each pose and also contains five mudras for identification of mudras postures. It contains around 500 images of each mudra. The feature has been extracted by making a skeleton on the body for yoga poses and a hand for mudra poses. Two different algorithms have been used for creating a skeleton: one for yoga poses and the second for hand mudras. Angles of the joints have been extracted as features for different machine learning and deep learning models. Among all the models, XGBoost with RandomSearch CV is the most accurate and gives 99.2% accuracy. The complete design framework is described in this article.