  Nowadays, yoga has become a part of life for many people. Exercises and
sports technological assistance is 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 include 10 Yoga
postures with around 400-900 images of each pose and also contain 5 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 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 a features for different machine learning and
deep learning models. among all the models XGBoost with RandomSearch CV is most
accurate and gives 99.2\% accuracy. The complete design framework is described
in the present paper.
