Paper Title
AI Yoga Assistant
Article Identifiers
: Authors
Keywords
Artificial Intelligence, YOGA, Deep Learning, Machine Learning
Abstract
In recent years, yoga has become an integral part of life for many individuals worldwide. Consequently, there is a growing need for the scientific examination of yoga postures. It has been noted that pose detection techniques can be employed to recognize postures and help individuals perform yoga more precisely. However, recognizing postures presents a challenge due to the scarcity of datasets and the difficulty of real-time posture detection. To address this issue, a comprehensive dataset containing at least 5500 images of ten distinct yoga poses has been compiled. A tf-pose estimation algorithm, which draws a skeletal representation of the human body in real-time, is utilized. Joint angles in the human body are extracted from the tf-pose skeleton and used as features for various machine learning models. 80% of the dataset is allocated for training, while 20% is reserved for testing. This dataset has been evaluated using different machine learning classification models, achieving a 99.04% accuracy with a Random Forest Classifier.
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How To Cite (APA)
Jaideep Solnia, Abhishek Chauhan, & Kanika Gola (May-2024). AI Yoga Assistant. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), g181-g188. https://ijnrd.org/papers/IJNRD2405624.pdf
Issue
Volume 9 Issue 5, May-2024
Pages : g181-g188
Other Publication Details
Paper Reg. ID: IJNRD_222381
Published Paper Id: IJNRD2405624
Downloads: 000122255
Research Area: Computer Science & TechnologyÂ
Author Type: Indian Author
Country: Ghaziabad, Uttar Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2405624.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405624
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