Computer Vision-Based Yoga Pose Recognition Using Hybrid Deep Learning Model

DOI

10.2991/978-94-6463-529-4_17How to use a DOI?

Keywords

Yoga asana; Human action recognition; Computer vision; Human pose estimation; Deep learning

Abstract

Human action recognition is a critical aspect of computer vision research and has various practical applications. In this paper, we focus on a specific type of action recognition, yoga pose recognition, and propose a computer vision-based model using deep learning. Our proposed model is a hybrid of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) and is designed to aid individuals in their self-practice of yoga. Mediapipe pose estimation is used to extract body keypoint as a feature of yoga poses. The Convolutional Neural Network (CNN) layer is utilized for extracting features from the keypoints, and the Gated Recurrent Unit (GRU) layer follows it to understand the sequence of frames for making predictions. The model is trained on video dataset of yoga poses carried out by various individuals. Model performance is evaluated based on its ability to accurately recognize the poses. The integration of Mediapipe and the combination of CNN and GRU offers a unique approach to yoga pose recognition and provides new insights into the field of human action recognition.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hukam Chand Saini
AU  - Renu Bagoria
AU  - Praveen Arora
PY  - 2024
DA  - 2024/10/04
TI  - Computer Vision-Based Yoga Pose Recognition Using Hybrid Deep Learning Model
BT  - Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)
PB  - Atlantis Press
SP  - 182
EP  - 193
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-529-4_17
DO  - 10.2991/978-94-6463-529-4_17
ID  - Saini2024
ER  -