Abstract:
In computer vision, human pose estimation is a deep-rooted issue that in the past has
revealed many challenges. In many fields such as security, video games, physical
therapy, etc. analyzing human activities is beneficial. One ofthe challenges in human
pose estimation is Yoga. These days, with stress and pressure full lives, people
generally prefer doing yoga at homes as yoga is said to be art ofrelaxation, but they
feel an instructor's need to evaluate their exercise form as doing wrong posture can
health problems. Since these resources are not always available, human pose
recognition can be used to create a system of self-training exercise that allows
individuals to better learn and practice exercises by their own.
This project objective is to develop an application which is an attempt to ensure correct
yoga posture for three main poses which includes plank, warrior and pose reverse
intuitive way. This project uses deep learning technique for pose
estimation in which different stages are involved including pre-processing stage, data
augmentation, creating CNN model and training the model. Yoga-guide's ultimate aim
is to use pose recognition as a tool to allow a person to practice different poses ofyoga
and classify the pose.
In this project, using convolution neural network (CNN) model using Keras with
TensorFlow as backend a deep learning model is proposed. The key benefit of using
this technique is that it offers extraction and identification offeatures that are
appropriate for pose recognition. We are able to achieve accuracy of 97%.
