JOURNAL ARTICLE

Detection and Estimation of Yoga Pose using Artificial Intelligence.

  • Published In: Grenze International Journal of Engineering & Technology (GIJET), 2024, v. 10, n. 1, Part 2. P. 1248 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Battur, Ranjana; Kunchur, Pavan; Bangarashetti, Sadhana; Managuli, Manjunath; Kulkarni, Gururaj L. 3 of 3

Abstract

Yoga offers a fresh perspective on our daily disciplines, including our physical, mental, and spiritual routines. Yoga has been around for a while. Medical experts and a large number of celebrities advise practicing yoga because of its various health benefits. A straightforward definition of yoga is to take care of your body, mind, and breath. However, depending on the COVID-19 condition, it is crucial to keep your health in check while everything takes place at home. You must work out every day. You need a home teacher to perform the exercise properly, something you cannot do in the COVID-19 environment. Lack of instruction when performing yoga poses is harmful to our health. So, in order to identify and detect the yoga posture form, we are going to offer a proposed model for pose recognition using machine learning. Our method is based on 8 yoga postures. Using the Tkinter library, we created a desktop application with a GUI. The media pipe and OpenCV library are used to preprocess the input as an image, recognize the item, and identify the human body's central organs. We use CSV files that are downloaded from Kaggle for the training and testing data. We train and test using logistic regression models. The system receives a perfect accuracy rating. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Grenze International Journal of Engineering & Technology (GIJET). 2024/01, Vol. 10, Issue 1, Part 2, p1248
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2024
  • ISSN:23955287
  • Accession Number:175658242
  • Copyright Statement:Copyright of Grenze International Journal of Engineering & Technology (GIJET) is the property of GRENZE Scientific Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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