AI-Powered Yoga Pose Detection and Feedback System

Abstract

Inordertoclosethegap between conventional yoga practices and contemporarytechnological breakthroughs, thisstudyintroducesanovelAI-drivenyoga pose identification and feedback system. With its roots in ancient customs, yoga has gained international recognition for its mental, spiritual, and physical health benefits. However, the individualized, real- timeinputthatisnecessarytomaintainsafe, proper alignment and posture during practice is frequently absent from the contemporary approach to yoga. This study presents a novel AI-powered solution. The solution tackles the major issues with both traditional and digital yoga training, with a reported accuracy of up to 97% under different settings. Users may instantly modify their postures with the platform\'s real-time feedback features, which lowers the chance of injury and increases the effectiveness of yoga sessions. The technology is made to work on everyday gadgets like laptops and cell phones.

Introduction

Yoga, with its ancient origins, is celebrated globally for promoting physical, mental, and spiritual well-being. However, traditional teaching methods often lack real-time feedback, especially in online or self-guided settings, leading to improper posture and injury risks. Recent advancements in artificial intelligence (AI), particularly in human pose estimation using deep learning and convolutional neural networks (CNNs), have significantly improved pose detection accuracy and real-time feedback.

This study presents an AI-driven yoga system that leverages technologies like MediaPipe and OpenCV to provide immediate, precise posture correction and breathing guidance through an accessible, multilingual interface. The system’s modular architecture supports scalability via cloud-based data management, making it suitable for both individual users and fitness studios.

A literature review highlights the evolution of pose estimation from costly depth cameras to efficient CNN models and discusses the integration of feedback mechanisms to enhance safety and user engagement. The proposed system addresses common issues in yoga practice by automating pose monitoring, reducing dependence on instructors, and operating on standard devices without specialized hardware.

User feedback from a survey of 100 participants demonstrated improved posture alignment, increased safety, and high usability. Compared to existing solutions, this approach excels in accuracy, real-time responsiveness, scalability, and cost-effectiveness, positioning it as a leading innovation in AI-powered wellness technology.

Conclusion

To sum up, the AI-powered yoga posture recognition and feedback system is a revolutionary development in the wellness technology space. The technology overcomes the drawbacks of self-guided training and conventional yoga practices by fusing cutting- edge machine learning algorithms with user- centric design principles. It is a useful tool for people,fitnessexperts,andcompaniesduetoits high accuracy, real-time feedback, and scalability. Thesystem\'s versatilitytomore generalfitness and wellness applications beyond yoga underscores its potential to revolutionize the waytechnologypromoteshealthandwellbeing. Thissystemispositionedtobecomeamainstay inthewellnesssectorasAIandIoTtechnologiesadvance,offeringusersallaround the world the advantages of individualized advice and real-time monitoring. Its influence goes beyond individual practitioners; it will continue to shape corporate health, rehabilitation, and fitness programs in the future.

References

[1] Yadav,S.K.,etal.\"Real-TimeYogaRecognition Using Deep Learning.\" Neural Computing and Applications, 2019. [2] Singh, N.T., et al. \"Real-Time Yoga Pose Detection Using OpenCV and MediaPipe.\" INCET Proceedings, 2023. [3] Choudhary, P., et al. \"Yoga Pose Detection and Feedback Generation: A Review.\" International Journal of Multidisciplinary Research, 2023. [4] Shotton, J., et al. \"Real-Time Human Pose Recognition in Parts from a Single Depth Image.\" Microsoft Research, 2011. [5] Cao, Z., et al. \"OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields.\" IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017. [6] Zhang, X., et al. \"DeepPose: Human Pose Estimation via Deep Neural Networks.\" IEEE Conference on Computer Vision and Pattern Recognition, 2014. [7] Liu, C., et al. \"Transformers in Vision: A Survey.\" IEEE Access, 2022. [8] Park, J.H., et al. \"IoT-Enabled Smart Mat for Yoga Pose Correction and Feedback.\" Sensors and Actuators, 2021. [9] Krizhevsky, A., et al. \"ImageNet Classification with Deep Convolutional Neural Networks.\" Advances in Neural Information Processing Systems, 2012. [10] Simonyan, K., et al. \"Very Deep Convolutional Networks for Large-Scale Image Recognition.\" arXiv preprint arXiv:1409.1556, 2014.

Copyright

Copyright © 2025 Aditya Garg, Abhay Bansal, Aditya Gupta, Abhinav Chaudhary, Jitendra Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.