Yoga Pose Recognition with Real time Correction using Deep Learning
Vinay Chethan Reddy Pala, Sreekar Kamatagi, Shyamsunder Jangiti, K. Swaraja, K. Reddy Madhavi, Gs Naveen Kumar
Abstract
In day-to-day life, it can be difficult for a person to devote his time to attend Yoga classes. In Yoga sessions, there might be a lack of individual attention for each person. While performing poses, incorrect muscle usage might lead to long-term muscle pain, back pain or many other deformities. To solve the aforementioned problems, a web application is built where a person can correct yoga pose. The Proposed methodology is working with TensorFlow lite Pose detection python module for recognizing human action based on Yoga Pose Classification using Image Processing and Deep Learning. The Objective of pose estimation is for monitoring the movement of human pose for distinct exercises. From this, the recognition of yoga poses can be done using backend part and wrongly recognized yoga poses can be corrected using frontend part. A real-time test is also carried out within a group of 5 people (three men and two women), and the accuracy attained is around 90%. Using deep learning, the proposed model accuracy is evaluated by fitting the training data and predicting it over the testing data which is estimated to be around 98%.