YogSaarthiAI: A Vision-Based AI System for Yoga Posture Analysis and Fitness Guide

Siddhi R. Bawankar, Atharva M. Borkar, Khushal T. Jangid, Aman S. Darda and M. K. Popat

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 2, 589-597

Abstract: The efficacy of yoga practice is fundamentally tied to precise postural alignment; however, the absence of real-time, personalized feedback in modern digital training platforms frequently leads to suboptimal execution and potential musculoskeletal injury. To bridge this critical gap, this paper introduces YogSaarthiAI, an advanced, real-time virtual yoga assistant that leverages computer vision and deep learning to deliver highly accurate, full-body posture analysis and dynamic correction. Unlike conventional fitness applications that rely on opaque, computationally heavy models or provide limited binary evaluations, YogSaarthiAI utilizes a highly optimized, dual-path hybrid architecture. The system processes live webcam video streams through the MediaPipe framework to extract 33 distinct skeletal landmarks, which are mathematically transformed into ten resolution-independent, scale-invariant geometric angle features. A custom-trained Deep Neural Network (DNN) classifies the user's movements across nine fundamental yoga poses, achieving a robust test accuracy exceeding 95% with a minimal inference time of approximately 10 milliseconds. Operating in parallel, a deterministic template-matching engine compares live biomechanical vectors against expert-derived pose blueprints to generate graduated, joint-specific corrective feedback. A significant contribution of this work is the engineering of a novel Symmetry Mirroring Heuristic, designed to resolve unilateral body occlusion during lateral poses (such as the Plank and Bridge), which empirically reduced false corrective alarms from 40% to under 5% during live execution. Deployed on an asynchronous FastAPI and ReactJS web infrastructure, the platform sustains an end-to-end frame processing latency of 200 to 400 milliseconds, ensuring instantaneous instructional delivery. Comprehensive system evaluations and user acceptance testing confirm that YogSaarthiAI provides objective, pedagogically valuable guidance, establishing a scalable, accessible, and computationally efficient paradigm for automated biomechanical assessment.

Keywords: Yoga Posture Analysis; Human Pose Estimation; Computer Vision; Deep Learning; Real-Time Feedback; Artificial Intelligence (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i2:id:1491

DOI: 10.32628/IJSRST2613331

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