Real-time Recognition of Yoga Poses using computer Vision for Smart Health Care

TL;DR

This paper presents a real-time computer vision system for recognizing yoga poses and hand mudras, achieving high accuracy and aiding users in correct posture execution for health benefits.

Contribution

It introduces a novel dataset and a framework for real-time yoga pose and mudra recognition using skeleton-based features and machine learning models.

Findings

XGBoost with RandomSearch CV achieves 99.2% accuracy.

Developed the YOGI dataset with 10 yoga poses and 5 mudras.

Framework enables real-time posture correction assistance.

Abstract

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.