ISSN2456-3315
Impact Factor10.57
UGC CAREApproved
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CrossRefDOI
ESTD2016
Peer ReviewTransparent
AccessOpen
Submission: Always Open
Paper Title
Pose Perfect - Identification and Correction of Yoga Poses Using CNN
Authors
M Madhavi , Shashank V , R Vaishnavi , S Abhinav
Keywords
Convolutional Neural Network, Layers, Computer Vision, Yoga Asana, Asana Alignment, Real-time Feedback
Abstract
This paper presents a novel approach to yoga pose recognition and correction, employing Convolutional Neural Networks (CNNs) for precise identification and alignment of yoga asanas. Leveraging Keras and TensorFlow frameworks, the model is trained on a diverse dataset of yoga pose images and videos, enabling accurate classification of various yoga postures. By integrating OpenCV, the system efficiently preprocesses video frames, extracts relevant features, and predicts the corresponding yoga pose in real-time. In instances where incorrect poses are detected, the system provides corrective feedback, guiding practitioners towards proper alignment and form. The proposed system aims to enhance the practice of yoga by offering personalized guidance and feedback, thereby promoting safety, effectiveness, and overall well-being for yoga enthusiasts of all levels. Future directions include expanding the dataset to encompass a wider range of yoga poses, refining the model architecture for improved accuracy, and integrating additional sensors or wearable devices to enhance real-time feedback capabilities. This project signifies a convergence of machine learning, computer vision, and yoga practice, with the potential to revolutionize the way yoga is learned and practiced in the digital age.
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How To Cite
"Pose Perfect - Identification and Correction of Yoga Poses Using CNN", IJRTI - International Journal for Research Trends and Innovation (www.IJRTI.org), ISSN:2456-3315, Vol.9, Issue 6, page no.268 - 277, June-2024, Available :https://ijrti.org/papers/IJRTI2406037.pdf
Issue
Volume 9 Issue 6,
June-2024
Pages : 268 - 277
Other Publication Details
Paper Reg. ID: IJRTI_189615
Published Paper Id: IJRTI2406037
Downloads: 205,635
Research Area: Engineering
Country: Hyderabad, Telangana, India
Published Paper PDF: https://ijrti.org/papers/IJRTI2406037
Published Paper URL: https://ijrti.org/viewpaperforall?paper=IJRTI2406037
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ISSN: 2456-3315 | IMPACT FACTOR: 10.57 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 10.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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