Yugyog: An Intelligent Real-Time Yoga Posture Detection and Correction System using Deep Learning and Computer Vision

Abstract

The rapid growth of digital platforms has trans- formed how individuals connect and interact; however, most existing systems are either socially oriented or professionally driven, lacking a balanced ecosystem that fosters meaningful collaboration,skillexchange,andpersonalgrowth.YugYogisan AI-powered intelligent networking platform designed to bridge this gap by enabling users to connect based on shared interests, skills, goals, and collaborative intent. Traditional platforms rely heavilyonsuperficialconnectionsorstaticprofileattributes,often resulting in irrelevant recommendations and low engagement quality. The proposed system integrates advanced technologies such as Natural Language Processing (NLP) for semantic profile understanding, machine learning-based hybrid recommendation systems for personalized matchmaking, and behavioral analytics to continuously adapt to user preferences. YugYog employs vector-basedsimilaritymode lstomatchusers contextually, ensur- ingmoremeaningfulandgoal-orientedconnections.Additionally, trust and authenticity are enhanced through reputation scoring mechanisms and activity-based validation. The platform also incorporates real-time interaction features, including intelligent chat assistance and community-based engagement modules, to facilitate seamless communication and collaboration. To ensure fairness and inclusivity, the system adopts bias-aware recom- mendation strategies and explainable AI techniques that provide transparency in how matches and suggestions are generated. Privacy-preserving mechanisms such as data encryption and controlled access further strengthen user trust. Compared to conventional networking applications, YugYog offers a more dynamic, adaptive, and user-centric experience that promotes learning, collaboration, and community building. This paper presentsthedesign,architecture,coremethodologies,andimpact of YugYog in redefining digital networking by creating a more meaningful, intelligent, and inclusive connection ecosystem [7],[8].

Introduction

The document presents YugYog, an intelligent networking platform designed to overcome limitations of traditional social and professional networking systems. Existing platforms rely on static profiles, basic matching, and lack contextual understanding, often resulting in irrelevant recommendations, poor user engagement, and low trust. YugYog addresses these issues using AI, Machine Learning, and NLP to enable context-aware, personalized, and meaningful user connections based on interests, skills, goals, and behavior.

The system uses a hybrid recommendation approach combining collaborative filtering and content-based filtering, along with semantic embeddings, cosine similarity, clustering (K-means), and logistic regression for prediction and grouping. It also integrates fairness-aware algorithms, explainable AI, and privacy/security mechanisms to ensure transparency, reduce bias, and protect user data. Continuous learning from user feedback improves recommendation quality over time, while anomaly detection enhances platform safety.

Key challenges in intelligent networking systems include bias, privacy risks, lack of context understanding, scalability, cold-start problems, and user engagement issues. YugYog proposes solutions such as standardized profiles, real-time dashboards, explainable recommendations, adaptive learning, and secure data handling.

Conclusion

YugYogpresents an effective and innovative solution tothe limitations of traditional yoga training methods by lever- aging advanced technologies such as Machine Learning and Computer Vision. The system enhances the overall practice experience by enabling real-time posture detection, angle- based analysis, and instant corrective feedback, thereby im- proving accuracy and reducing the risk of injury during unsupervisedyogasessions.Theproposedplatformbridges the gap between guided instruction and independent practice by providing an interactive and user-friendly environment. Key features such as live skeletal visualization, intelligent feedback generation, and performance analytics contribute toa more engaging and data-driven approach to yoga training. Additionally, the web-based deployment ensures accessibility and ease of use, making the system suitable for a wide rangeofusers,includingbeginnerspracticingathome.Experimental results demonstrate that YugYog achieves high pose detection accuracy (approximately 90–95%) along with smooth real- time performance. The incorporation of analytics and session- basedevaluationfurthersupportscontinuousimprovementand user motivation. Looking ahead, the system can be extended by incorporating a wider range of yoga poses, improving robustness under varying environmental conditions, and inte- gratingwearablesensorsforenhancedprecision.Furthermore, the inclusion of personalized training plans and long-term progress tracking can significantly enhance user experience.In conclusion, YugYogestablishes a strong foundation for intelligent, accessible, and safe yoga practice by combining AI-driven techniques with real-time user interaction, paving the way for future advancements in smart fitness and health monitoring systems [1],[6].

References

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Copyright

Copyright © 2026 Praveen Singh Rawat, Abhinav Singh, Shivam Kadam, Sarthak Singh, Archana Pakhare . 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.