Real Time Yoga Pose Detection Using AI
Article Sidebar
Main Article Content
Abstract—After the pandemic situation in the world, a new era started where everything is going online like education, training, shopping, work, etc. by taking the advantages of AI technology and the online trend of doing YOGA at home, Bringing the idea, there is a chance to shift offline yoga training to online. This study introduces a novel method to detect real-time yoga poses using artificial intelligence (AI) and camera input and provide corrective feedback accordingly. The method uses a real-time pose prediction model, using the advantage of Media Pipe to capture human body landmarks from camera frames. These key points are then analyzed to identify different yoga poses. The system provides real-time corrective feedback on posture by analyzing it to help users make sure that it works smoothly. By combining AI-based pose recognition and feedback, this solution aims to improve yoga practice, especially for beginners.This research presents a deep learning-based approach for yoga pose detection using the YOLOv5 object detection framework. The data set, named YOLO Yoga Dataset.v1i.yolov5, consists of 1,013 annotated images in five yoga poses: Bridge, Downward Dog, Plank, Shoulderstand and Tree Pose. The data set was sourced and preprocessed through Roboflow and trained on YOLOv5 with data augmentation to improve generalization.
Downloads
References
K. R, V. R and M. A, ”Yoga Asanas Pose Detection using Feature Level Fusion with Deep Learning-Based Model,” 2022 International Conference on computational Modelling, Simulation and Optimiza- tion (ICCMSO), Pathum Thani, Thailand, 2022, pp. 331-339, doi: 10.1109/ICCMSO58359.2022.00071.
Kumar et al. (2020). Exercise Posture Monitoring System using Pose Estimation. International Journal of Scientific and Technol- ogy Research. [Link](http://www.ijstr.org/final-print/nov2020/Exercise- Posture-Monitoring-System-Using-Pose-Estimation.pdf)
V. Agarwal, K. Sharma and A. K. Rajpoot, ”AI based Yoga Trainer Simplifying home yoga using mediapipe and video streaming,” 2022 3rd International Conference for Emerging Technology (INCET), Belgaum, India, 2022, pp. 1-5, doi: 10.1109/INCET54531.2022.9824332.
S. A. Elavarasi, P. Ankit Kumar and J. Jayanthi, ”Development of Ai- Based Posture Monitoring System to Assist Yoga Training,” 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineer- ing (RMKMATE), Chennai, India, 2023, pp. 1-4, doi: 10.1109/RMK- MATE59243.2023.10368735.
Chaudhary, N. Thoiba Singh, M. Chaudhary and K. Yadav, ”Real- Time Yoga Pose Detection Using OpenCV and MediaPipe,” 2023 4th International Conference for Emerging Technology (INCET), Belgaum, India, 2023, pp. 1-5, doi: 10.1109/INCET57972.2023.10170485.
H. Dhakate, S. Anasane, S. Shah, R. Thakare and S. G. Rawat, ”Enhancing Yoga Practice: Real-time Pose Analysis and Personalized Feedback,” 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC), Bhubaneswar, India, 2024, pp. 35-40, doi: 10.1109/ESIC60604.2024.10481659.
Q. Su, J. Zhang, M. Chen and H. Peng, ”PW-YOLO-Pose: A Novel Algorithm for Pose Estimation of Power Workers,” in IEEE Access, vol. 12, pp. 116841-116860, 2024, doi: 10.1109/ACCESS.2024.3437359.
R. S. Alruwaythi, T. A. Alruwaili, R. M. Alsehli and L. Syed, ”Enhancing Pedestrian Pose Detection through YOLO Deep Learning Techniques,” 2024 17th International Conference on Development in eSystem Engineering (DeSE), Khorfakkan, United Arab Emirates, 2024, pp. 509-514, doi: 10.1109/DeSE63988.2024.10911952.
W. Supanich, S. Kulkarineetham, P. Sukphokha and P. Wisarn- sart, ”Machine Learning-Based Exercise Posture Recognition Sys- tem Using MediaPipe Pose Estimation Framework,” 2023 9th In- ternational Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2023, pp. 2003-2007, doi: 10.1109/ICACCS57279.2023.10112726.
S. Ranjan, S. Tyagi, S. Gupta, M. Kaur and M. Kumar Goyal, ”Image Processing-Based Real-Time Detection and Tracking of Human,” 2023 3rd International Conference on Technological Advancements in Com- putational Sciences (ICTACS), Tashkent, Uzbekistan, 2023, pp. 1542- 1547, doi: 10.1109/ICTACS59847.2023.10390106
Google, “MediaPipe: Cross-platform, customizable ML solutions for live and streaming media,” https://mediapipe.dev/, Accessed: Mar. 2025
L. J. Wilson, S. Patel and A.S. Sharma, ’Real-time human pose detection for fitness applications’, Journal of AI Research, vol. 45, pp. 125-134, Mar. 2023.
T. J. Nakamura, S. D. Moore, and K. L. Henson, “Leveraging machine learning for real-time pose correction in yoga,” IEEE Transactions on Biomedical Engineering, vol. 72, no. 6, pp. 832-841, Jun. 2024.
Z. Cao, G. Hidalgo, T. Simon, S. Wei, and Y. Sheikh, ”OpenPose: Realtime multi-person 2D pose estimation using part affinity fields,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 1, pp. 172-186, Jan. 2021.
TensorFlow, ”Pose estimation with TensorFlow models,” https://www.tensorflow.org/lite/models/poseestimation, Accessed : Mar.2025.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.