A Computer Vision Assisted Yoga Trainer for a Naive Performer by Using Human Joint Detection

Abstract

It is a well-known proverb that a healthy mind lives in a healthy body, and yoga is one such means for connecting the body to the mind. However, yoga should be performed under professional supervision and in a regulated manner, as it can be harmful to one's health if done incorrectly. Moreover, it is difficult for beginners to identify the incorrect portions of their yoga postures on their own. In this research article, we present a user-friendly python-flask based web application that assists its registered users to perform every pose accurately. We have used computer vision techniques as it can perform various visual data frame related operations in real time. Our method consists of two main components: a hand gesture component that records video using hand gestures and a pose estimation component that detects body joint coordinates. The system then compares the angles obtained from the instructor's pose and the users for feedback generation and provides correction if the difference is larger than a certain threshold. With this inherent capability of pose feedback generation, the proposed system thus enables the naive performers to evaluate their poses and correct it when it deviates from the correct pose sequence. The method was evaluated in real-time on people of varied age groups and gender for four different asanas, and it was proven that it recognizes incorrect portions of the performed asanas for all the test cases. The experimental findings in terms of feedback generated using the user videos gave a functional validation of the proposed procedure and its usability in modern day human life.