International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 08 | Aug 2022
p-ISSN: 2395-0072
www.irjet.net
”YOGA WITH AI” Prathamesh Mishra1 Student, Thakur College Of Engineering And Technology ,Maharashtra ,India ---------------------------------------------------------------------***--------------------------------------------------------------------have given precision of 69.4 % of pose estimation and 68.9% Abstract - Yoga plays a vital importing lucky person for post tracking hence there is a chance of improvement always.
mentally and physically fit with the help of yoga both can be done. Not only help us to stay fit mentally but also physically spiritual exercises can help us to cure some diseases 100%. yoga is consisted of different asana that is posture. each postures have its own benefit significance.
1.2 bottom-up method A)
Key Words: Artificial intelligence,Yoga, Human Pose Estimation ,Yoga Pose Classification
1. INTRODUCTION In This project we used human pose estimation and deep learning in order to train our model estimation of human poses can be classified into two types-
2. REVIEW OF LITERATURE 2.1 PoseNet -is another deep learning framework similar to OpenPose which is used for identification of human poses in images or video sequences by identifying joint locations in a human body. These joint locations or keypoints are indexed by
1) discriminative = first one is discriminative in this estimation of human pose with the help of image (static and stable objects)
Deep learning overview - a vital aspect in deep learning is built on artificial neural network. Start to end architecture is provided by deep learning for reading some key information from the given data set (image videos etc.). Different techniques and methods for identifying those human poses
"Part ID” which is a confidence score whose value lies in the range of 0.0 and 1.0 with 1.0 being the greatest. The PoseNet model’s performance varies depending on the device and output stride [14]. The PoseNet model is invariant to the size of the image, thus it can predict pose positions in the scale of the actual image irrespective of whether the image has been downscaled.
2) generative= it contains posters which include moving objects (moving up and down or side wise)
In PoseNet, the SoftMax layer is replaced by a sequence of fully connected layers. A high-level architecture of PoseNet is shown in Fig. 1 The first component in the architecture is an encoder which is responsible for generating the encoding vector v, a 1024-dimensional vector that is an encoded representation of the features of the input image. The second component is the localizer which generates vector u which denotes localization features. The last component is a regressor which consists of two connected layers that are used to regress the final pose.
Index Terms—Human pose estimation, yoga, OpenPose, ma- chine learning, deep learning. 1.1 up -down method A) Up -down method- it is the most commonly used method basically it has the feature of breaking the main task into smaller and multiple parts of the given task. those smaller parts include identifying the pose that is the object analyzing the pose.
2.2 OpenPose: It was created in Carnegie melon university. The standard feature of openpose is that there is multiple purpose , multi person, real-time detection programs which has changed the entire world of pose detection. It is inclusive of ears, eyes, neck, nose, elbows, shoulders knees, wrist, ankle , hips etc. key points.
It has three basic principles 1) human candidate detector 2) analyzing human candidates 3) tracking the human poses
It is widely used in sports surveillance, pose detection , activity detection, health yoga, and pose identification.
Primary motive is to identify the human (candidate) letter it starts tracking the human pose some researchers
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Bottom-up method- in this web only focus on the key points in the human body that is the subject and then we organize it into several data mechanism it primarily focuses on the numbers of subjects in the image all the important features are taken from the data (image)
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