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Real-time yoga pose classification with 3-D pose estimation model with LSTM

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Abstract

Yoga has now become a part of life for humans all over the globe enabling them to unite the body with the mind, helping in achieving a healthy lifestyle in the modern day. Practicing yoga with a trainer is always recommended to get maximum benefit but with this fast-paced lifestyle getting time for a lesson with a trainer or under proper guidance gets difficult and also sometimes middle-class families cannot bear the cost of a trainer. Therefore, a system is required which is accessible to everyone and can help in performing yoga poses and improving. This paper introduces a novel framework developed for estimating yoga poses through computer vision using a 3-D top-down semantic key landmark estimator with a Recurrent Neural Network (RNN) for classification. For training and validation of our model, we tailored our custom dataset of 10 different yoga poses having a total of 300 sequences. The model on the dataset gave an average of 92.34% accuracy using Long Short-Term Memory (LSTM) classifier.

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Data availability

Data available on request from the authors. The data that support the findings of this study are available from the corresponding author [Dr. Lokendra Singh Umrao], upon reasonable request. Policy: Basic, Share upon Request

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Authors and Affiliations

  1. CSIT Department, Guru Ghasidas Vishwavidyala, Bilaspur, India

    Ratnesh Prasad Srivastava

  2. Department of Computer Science and Engineering, Institute of Engineering & Technology, Dr. Rammanohar Lohia Avadh University, Ayodhya, Faizabad, India

    Lokendra Singh Umrao

  3. Department of Business Management & Entrepreneurship, Dr. Rammanohar Lohia Avadh University, Ayodhya, India

    Ramjeet Singh Yadav

Authors
  1. Ratnesh Prasad Srivastava
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  2. Lokendra Singh Umrao
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  3. Ramjeet Singh Yadav
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Correspondence to Lokendra Singh Umrao.

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Srivastava, R.P., Umrao, L.S. & Yadav, R.S. Real-time yoga pose classification with 3-D pose estimation model with LSTM. Multimed Tools Appl 83, 33019–33030 (2024). https://doi.org/10.1007/s11042-023-17036-8

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  • DOI: https://doi.org/10.1007/s11042-023-17036-8

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